Merge pull request #1041 from dfalbel/v2.3.0.0-rc0

Prepare for the 2.3.0.0 release

Author: dfalbel

.github/workflows/main.yaml

@@ -12,7 +12,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ['windows-latest', 'macOS-latest', 'ubuntu-16.04']
-        tf: ['1.14.0', '1.15.2', '2.0.1', '2.1.0', '2.2-rc3', 'nightly']
+        tf: ['1.14.0', '1.15.2', '2.0.1', '2.1.0', '2.2.0', 'nightly']
         include:
           - os: ubuntu-16.04
             cran: https://demo.rstudiopm.com/all/__linux__/xenial/latest

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: keras
 Type: Package
 Title: R Interface to 'Keras'
-Version: 2.2.5.0
+Version: 2.3.0.0
 Authors@R: c(
   person("Daniel", "Falbel", role = c("ctb", "cph", "cre"), email = "[email protected]"),
   person("JJ", "Allaire", role = c("aut", "cph")),

R/history.R

@@ -17,9 +17,13 @@ print.keras_training_history <- function(x, ...) {
     validate <- paste0(", validated on ", params[["validation_samples"]], " samples")
   else 
     validate <- ""
-  str <- paste0("Trained on ", params[["samples"]]," samples", validate, " (batch_size=", 
-                params[["batch_size"]], ", epochs=", params[["epochs"]], ")")
-
+  
+  str <- ""
+  if (!params[["samples"]] == "NULL") {
+    str <- paste0(str, "Trained on ", params[["samples"]]," samples", validate, " (batch_size=", 
+                  params[["batch_size"]], ", epochs=", params[["epochs"]], ")")
+  }
+    
   # last epoch metrics
   metrics <- lapply(x$metrics, function(metric) {
     metric[[epochs]]

R/install.R

@@ -68,7 +68,7 @@
 #' desired backend) as described on the [Keras website](https://keras.io/#installation)
 #' and the Keras R package will find and use that version. 
 #' 
-#' See the documentation on [custom installations](https://tensorflow.rstudio.com/installation.html#custom-installation)
+#' See the documentation on [custom installations](https://tensorflow.rstudio.com/installation/custom/)
 #' for additional information on how version of Keras and TensorFlow are located
 #' by the Keras package.
 #'

man/get_layer.Rd

@@ -73,7 +73,7 @@ CustomLayer <- R6::R6Class("CustomLayer",
 )
 ```
 
-Note that tensor operations are executed using the Keras `backend()`. See the [Keras Backend](backend.html) article for details on the various functions available from Keras backends.
+Note that tensor operations are executed using the Keras `backend()`. See the Keras Backend article for details on the various functions available from Keras backends.
 
 ## Layer Wrapper Function
 

man/install_keras.Rd

@@ -503,7 +503,7 @@ The VGG16 model is also the basis for the [Deep dream](https://keras.rstudio.com
 
 ## How can I use other Keras backends?
 
-By default the Keras Python and R packages use the TensorFlow backend. Other available backends include Theano or CNTK. To learn more about using alternatate backends (e.g. Theano or CNTK) see the [article on Keras backends](backend.html).
+By default the Keras Python and R packages use the TensorFlow backend. Other available backends include Theano or CNTK. To learn more about using alternatate backends (e.g. Theano or CNTK) see the article on Keras backends.
 
 
 ## How can I use the PlaidML backend?

vignettes/custom_layers.Rmd

@@ -75,7 +75,7 @@ model %>%
 There are many `layers` available with some common constructor
 parameters:
 
-  - `activation`: Set the [activation function](https://tensorflow.rstudio.com/keras/reference/#section-activation-layers) for the layer. By default, no activation is applied.
+  - `activation`: Set the [activation function](https://tensorflow.rstudio.com/reference/keras/#section-activation-layers) for the layer. By default, no activation is applied.
   - `kernel_initializer` and `bias_initializer`: The initialization
     schemes that create the layer’s weights (kernel and bias). This defaults to the
     [`Glorot uniform`](https://tensorflow.rstudio.com/keras/reference/initializer_glorot_uniform.html) initializer.

vignettes/faq.Rmd

@@ -57,7 +57,7 @@ install.packages("keras")
 install_keras()
 ```
 
-This will provide you with default CPU-based installations of Keras and TensorFlow. If you want a more customized installation, e.g. if you want to take advantage of NVIDIA GPUs, see the documentation for `install_keras()` and the [installation](/installation/) section.
+This will provide you with default CPU-based installations of Keras and TensorFlow. If you want a more customized installation, e.g. if you want to take advantage of NVIDIA GPUs, see the documentation for `install_keras()` and the [installation](https://tensorflow.rstudio.com/installation/) section.
 
 ## MNIST Example