Added install_autokeras function

NAMESPACE

@@ -4,18 +4,21 @@ S3method(export_autokeras_model,AutokerasModel)
 S3method(export_keras_model,AutokerasModel)
 S3method(final_fit,AutokerasModel)
 S3method(get_keras_model,AutokerasModel)
-S3method(predict,AutokerasModel)
 export(evaluate.AutokerasModel)
 export(export_autokeras_model)
 export(export_keras_model)
 export(final_fit)
 export(fit.AutokerasModel)
 export(get_keras_model)
+export(install_autokeras)
 export(model_image_classifier)
 export(model_image_regressor)
 export(model_tabular_classifier)
 export(model_tabular_regressor)
 export(model_text_classifier)
 export(model_text_regressor)
+export(predict.AutokerasModel)
+importFrom(keras,install_keras)
 importFrom(keras,load_model_hdf5)
 importFrom(reticulate,import)
+importFrom(reticulate,py_config)

R/final-fit.R

@@ -9,6 +9,7 @@
 # @param trainer_args A dictionary containing the parameters of the
 # ModelTrainer constructor.
 #' @param retrain A boolean of whether reinitialize the weights of the model.
+#' @param time_limit The time limit to fit in seconds.
 #'
 #' @name final_fit
 NULL
@@ -22,9 +23,13 @@ final_fit <- function(object, ...) {
 #' @rdname final_fit
 #' @export
 final_fit.AutokerasModel <- function(autokeras_model, x_train, y_train,
-                                     x_test, y_test, retrain=FALSE) {
-  autokeras_model@model$final_fit(x_train=x_train, y_train=y_train,
-                                  x_test=x_test, y_test=y_test,
-                                  retrain=retrain);
+                                     x_test, y_test, retrain=FALSE,
+                                     time_limit=Inf) {
+  setTimeLimit(elapsed=time_limit);
+  try({
+    autokeras_model@model$final_fit(x_train=x_train, y_train=y_train,
+                                    x_test=x_test, y_test=y_test,
+                                    retrain=retrain);
+  })
   return(invisible(autokeras_model));
 }

R/install.R

@@ -0,0 +1,123 @@
+
+#' Install Auto-Keras, Keras, and the TensorFlow backend
+#'
+#' Auto-Keras, Keras, and TensorFlow will be installed into an "r-tensorflow"
+#' virtual or conda environment. Note that "virtualenv" is not available on
+#' Windows (as this isn't supported by TensorFlow).
+#'
+# @inheritParams tensorflow::install_tensorflow
+#' @inheritParams keras::install_keras
+#'
+#' @param version Version of Auto-Keras to install. Specify "default" to install
+#'   the latest release. Otherwise specify an alternate version (e.g. "0.3.5").
+#'   The default value is "0.3.7" as it is the latest tested version.
+#'
+#' @param keras Keras version to install. Specify "default" to install
+#'   the latest release. Otherwise specify an alternate version (e.g. "2.2.2").
+#'
+#' @section GPU Installation:
+#'
+#' Keras and TensorFlow can be configured to run on either CPUs or GPUs. The CPU
+#' version is much easier to install and configure so is the best starting place
+#' especially when you are first learning how to use Keras. Here's the guidance
+#' on CPU vs. GPU versions from the TensorFlow website:
+#'
+#' - *TensorFlow with CPU support only*. If your system does not have a NVIDIA®
+#' GPU, you must install this version. Note that this version of TensorFlow is
+#' typically much easier to install, so even if you have an NVIDIA GPU, we
+#' recommend installing this version first.
+#'
+#' - *TensorFlow with GPU support*. TensorFlow programs typically run
+#' significantly faster on a GPU than on a CPU. Therefore, if your system has a
+#' NVIDIA® GPU meeting all prerequisites and you need to run
+#' performance-critical applications, you should ultimately install this
+#' version.
+#'
+#' To install the GPU version:
+#'
+#' 1) Ensure that you have met all installation prerequisites including
+#'    installation of the CUDA and cuDNN libraries as described in
+#'    [TensorFlow GPU Prerequistes](https://tensorflow.rstudio.com/installation_gpu.html#prerequisites).
+#'
+#' 2) Pass `tensorflow = "gpu"` to `install_autokeras()`. For example:
+#'
+#'     ```
+#'       install_autokeras(tensorflow="gpu")
+#'     ````
+#'
+#' @section Windows Installation:
+#'
+#' The only supported installation method on Windows is "conda". This means that
+#' you should install Anaconda 3.x for Windows prior to installing Keras.
+#'
+#' @section Custom Installation:
+#'
+#' Installing Keras and TensorFlow using `install_autokeras()` isn't required
+#' to use the Keras R package. You can do a custom installation of Keras (and
+#' 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)
+#' for additional information on how version of Keras and TensorFlow are located
+#' by the Keras package.
+#'
+#' @section Additional Packages:
+#'
+#' If you wish to add additional PyPI packages to your Keras / TensorFlow
+#' environment you can either specify the packages in the `extra_packages`
+#' argument of `install_autokeras()`,  or alternatively install them into an
+#' existing environment using the [reticulate::py_install()] function.
+#'
+#' @examples
+#' \dontrun{
+#'
+#' # default installation
+#' library("autokeras")
+#' install_autokeras()
+#'
+#' # install using a conda environment (default is virtualenv)
+#' install_autokeras(method="conda")
+#'
+#' # install with GPU version of TensorFlow
+#' # (NOTE: only do this if you have an NVIDIA GPU + CUDA!)
+#' install_autokeras(tensorflow="gpu")
+#'
+#' # install a specific version of TensorFlow
+#' install_autokeras(tensorflow="1.2.1")
+#' install_autokeras(tensorflow="1.2.1-gpu")
+#'
+#' # install a specific version of Keras and TensorFlow
+#' install_autokeras(keras="2.2.2", tensorflow="1.2.1")
+#'
+#' }
+#'
+#' @importFrom keras install_keras
+#' @importFrom reticulate py_config
+#'
+#' @export
+install_autokeras <- function(method=c("auto", "virtualenv", "conda"),
+                              conda="auto",
+                              version="0.3.7", # R autokeras built with this ver
+                              keras="default",
+                              tensorflow="default",
+                              extra_packages=NULL) {
+
+  if (as.numeric(reticulate::py_config()$version) < 3.6)
+    stop("Currently, Auto-Keras is only compatible with: Python 3.6.",
+         "Please update Python version and re-run install_autokeras()",
+         call.=FALSE)
+
+  # resolve version
+  if (identical(version, "default"))
+    version <- ""
+  else
+    version <- paste0("==", version)
+
+  # perform the install
+  install_keras(method=method,
+                conda=conda,
+                version=keras,
+                tensorflow=tensorflow,
+                extra_packages=c(paste0("autokeras", version), extra_packages))
+}

R/model-image-classifier.R

@@ -5,6 +5,11 @@
 #' It is used for image classification. It searches convolutional neural
 #' network architectures for the best configuration for the image dataset.
 #'
+#' Important: The object returned by this function behaves like an R6 object,
+#' i.e., within function calls with this object as parameter, it is most likely
+#' that the object will be modified. Therefore it is not necessary to assign
+#' the result of the functions to the same object.
+#'
 #' @param path A path to the directory to save the classifier as well as
 #' intermediate results.
 #' @param verbose A boolean of whether the search process will be printed to

R/model-image-regressor.R

@@ -5,6 +5,11 @@
 #' It is used for image regression. It searches convolutional neural network
 #' architectures for the best configuration for the image dataset.
 #'
+#' Important: The object returned by this function behaves like an R6 object,
+#' i.e., within function calls with this object as parameter, it is most likely
+#' that the object will be modified. Therefore it is not necessary to assign
+#' the result of the functions to the same object.
+#'
 #' @param path A path to the directory to save the classifier as well as
 #' intermediate results.
 #' @param verbose A boolean of whether the search process will be printed to

R/model-tabular-classifier.R

@@ -3,6 +3,11 @@
 #'
 #' TabularClassifier class.
 #'
+#' Important: The object returned by this function behaves like an R6 object,
+#' i.e., within function calls with this object as parameter, it is most likely
+#' that the object will be modified. Therefore it is not necessary to assign
+#' the result of the functions to the same object.
+#'
 #' @param path A path to the directory to save the classifier as well as
 #' intermediate results.
 #' @param verbose A boolean of whether the search process will be printed to

R/model-tabular-regressor.R

@@ -3,6 +3,11 @@
 #'
 #' TabularRegressor class.
 #'
+#' Important: The object returned by this function behaves like an R6 object,
+#' i.e., within function calls with this object as parameter, it is most likely
+#' that the object will be modified. Therefore it is not necessary to assign
+#' the result of the functions to the same object.
+#'
 #' @param path A path to the directory to save the classifier as well as
 #' intermediate results.
 #' @param verbose A boolean of whether the search process will be printed to

R/model-text-classifier.R

@@ -3,6 +3,11 @@
 #'
 #' A TextClassifier class based on Google AI's BERT model.
 #'
+#' Important: The object returned by this function behaves like an R6 object,
+#' i.e., within function calls with this object as parameter, it is most likely
+#' that the object will be modified. Therefore it is not necessary to assign
+#' the result of the functions to the same object.
+#'
 #' @param path A path to the directory to save the classifier as well as
 #' intermediate results.
 # @param device Specific hardware for using/running the model. E.g:- CPU, GPU

R/model-text-regressor.R

@@ -3,6 +3,11 @@
 #'
 #' TextRegressor class.
 #'
+#' Important: The object returned by this function behaves like an R6 object,
+#' i.e., within function calls with this object as parameter, it is most likely
+#' that the object will be modified. Therefore it is not necessary to assign
+#' the result of the functions to the same object.
+#'
 #' @param path A path to the directory to save the classifier as well as
 #' intermediate results.
 #' @param verbose A boolean of whether the search process will be printed to

R/package.R

@@ -19,18 +19,18 @@ autokeras <- NULL
 .onLoad <- function(libname, pkgname) {
   # browser()
   # delay load keras
-  autokeras <<- reticulate::import("autokeras", delay_load = list(
-    # todo: remove? priority, environment, and get_module not documented in
+  autokeras <<- reticulate::import("autokeras", delay_load=list(
+    # todo: remove? priority, and get_module not documented in
     # reticulate package
-    priority = 10,
-    environment = "r-tensorflow",
-    get_module = function() {
+    priority=10,
+    environment="r-tensorflow",
+    get_module=function() {
       "autokeras"
     },
-    on_load = function() {
+    on_load=function() {
       NULL
     },
-    on_error = function(e) {
+    on_error=function(e) {
       NULL
     }
   ))

R/predict.R

@@ -8,7 +8,8 @@
 NULL
 
 #' @rdname predict
-#' @export
+# @export
+#' @export predict.AutokerasModel
 predict.AutokerasModel <- function(autokeras_model, x_test) {
   autokeras_model@model$predict(x_test=x_test);
 }

README.Rmd

@@ -14,10 +14,7 @@ learning models.
 
 ## Dependencies
 
-* have fully functional [Auto-Keras](https://autokeras.com/), i.e. TensorFlow, 
-Keras, etc.
-
-* have fully functional TensorFlow and Keras R libraries.
+* [Auto-Keras](https://autokeras.com/) requires Python 3.6 .
 
 ## Installation
 
@@ -30,11 +27,18 @@ if (!require("devtools"))
 devtools::install_github("jcrodriguez1989/autokeras")
 ```
 
+Then, use the `install_autokeras()` function to install TensorFlow:
+
+```{r eval=FALSE}
+library("autokeras")
+install_autokeras()
+```
+
 ## Examples
 
 ### CIFAR-10 dataset
 
-```{r eval=TRUE}
+```{r eval=FALSE}
 library("autokeras")
 library("keras")
 
@@ -45,26 +49,26 @@ c(x_train, y_train) %<-% cifar10$train
 c(x_test, y_test) %<-% cifar10$test
 ```
 
-```{r eval=TRUE, include=FALSE}
+```{r eval=FALSE, include=FALSE}
 x_train <- x_train[1:1000,,,]
 y_train <- y_train[1:1000]
 x_test <- x_test[1:1000,,,]
 y_test <- y_test[1:1000]
 ```
 
-```{r eval=TRUE}
-# Create an image classifier, and train different models for 3 minutes
+```{r eval=FALSE}
+# Create an image classifier, and train different models for 2 hours
 clf <- model_image_classifier(verbose=TRUE, augment=FALSE) %>% 
-  fit(x_train, y_train, time_limit=3*60)
+  fit(x_train, y_train, time_limit=2*60*60)
 ```
 
 ```{r eval=FALSE}
 # Get the best trained model
 # For some bug, this needs to be killed (Ctrl+c), but fits the model anyways
-clf %>% final_fit(x_train, y_train, x_test, y_test, retrain=TRUE)
+clf %>% final_fit(x_train, y_train, x_test, y_test, retrain=TRUE, time_limit=30)
 ```
 
-```{r eval=TRUE}
+```{r eval=FALSE}
 # And use it to evaluate, predict
 clf %>% evaluate(x_test, y_test)
 clf %>% predict(x_test[1:10,,,])

README.md

@@ -6,9 +6,7 @@ R interface to Auto-Keras
 Dependencies
 ------------
 
--   have fully functional [Auto-Keras](https://autokeras.com/), i.e. TensorFlow, Keras, etc.
-
--   have fully functional TensorFlow and Keras R libraries.
+-   [Auto-Keras](https://autokeras.com/) requires Python 3.6 .
 
 Installation
 ------------
@@ -21,6 +19,13 @@ if (!require("devtools"))
 devtools::install_github("jcrodriguez1989/autokeras")
 ```
 
+Then, use the `install_autokeras()` function to install TensorFlow:
+
+``` r
+library("autokeras")
+install_autokeras()
+```
+
 Examples
 --------
 
@@ -46,7 +51,7 @@ clf <- model_image_classifier(verbose=TRUE, augment=FALSE) %>%
 ``` r
 # Get the best trained model
 # For some bug, this needs to be killed (Ctrl+c), but fits the model anyways
-clf %>% final_fit(x_train, y_train, x_test, y_test, retrain=TRUE)
+clf %>% final_fit(x_train, y_train, x_test, y_test, retrain=TRUE, time_limit=30)
 ```
 
 ``` r