2024

.github/workflows/keras_tuner_stable.yml

@@ -12,10 +12,10 @@ jobs:
       fail-fast: false
       matrix:
         config:
-        - { os: macOS-latest, tf: '2.13.0', keras-tuner: '1.3.5'}
-        - { os: windows-latest, tf: '2.13.0', keras-tuner: '1.3.5'}
-        - { os: windows-latest, tf: 'nightly', keras-tuner: '1.3.5'}
-        - { os: macOS-latest, tf: 'nightly', keras-tuner: '1.3.5'}
+        - { os: macOS-latest, tf: '2.16.1', keras-tuner: '1.4.7'}
+        - { os: windows-latest, tf: '2.16.1', keras-tuner: '1.4.7'}
+        - { os: windows-latest, tf: 'nightly', keras-tuner: '1.4.7'}
+        - { os: macOS-latest, tf: 'nightly', keras-tuner: '1.4.7'}
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       TF_VERSION: ${{ matrix.config.tf }}

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: kerastuneR
 Type: Package
 Title: Interface to 'Keras Tuner' 
-Version: 0.1.0.6
+Version: 0.1.0.7
 Authors@R: c(
   person("Turgut", "Abdullayev", role = c("aut", "cre"),
          email = "[email protected]"),  
@@ -20,7 +20,7 @@ BugReports: https://github.com/EagerAI/kerastuneR/issues/
 SystemRequirements: TensorFlow >= 2.0 (https://www.tensorflow.org/)
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Imports:
     reticulate, 
     tensorflow,
@@ -35,7 +35,7 @@ Imports:
     crayon,
     magick
 Suggests:
-    keras,
+    keras3,
     knitr,
     tfdatasets,
     testthat,

R/install.R

@@ -21,7 +21,7 @@ install_kerastuner <- function(version = NULL, ..., bayesian = TRUE, restart_ses
 
 
   if (is.null(version) & !from_git) {
-    module_string <- paste0("keras-tuner==", '1.3.5')
+    module_string <- paste0("keras-tuner==", '1.4.7')
   } else if (!is.null(version)) {
     module_string <- paste0("keras-tuner==", version)
   } else if (isTRUE(from_git)) {

R/random_search.R

@@ -78,7 +78,8 @@ RandomSearch = function(hypermodel, objective, max_trials, seed = NULL,
   if(is.null(hyperparameters))
     args$hyperparameters <- NULL
 
-
+  if(!is.null(args$executions_per_trial))
+    args$executions_per_trial <- as.integer(args$executions_per_trial)
 
   do.call(kerastuner$tuners$RandomSearch, args)
 

README.md

@@ -64,7 +64,7 @@ y_data2 <-  ifelse(runif(50,0,1) > 0.6, 1L,0L) %>% as.matrix()
 This function returns a compiled model.
 
 ```
-library(keras)
+library(keras3)
 library(tensorflow)
 library(kerastuneR)
 

cran-comments.md

@@ -4,7 +4,7 @@ This is a 4th submission of 'kerastuneR' 0.1.0.6
 
 * Github actions: windows-latest
 * Github actions: macOS-latest
-* Github actions: ubuntu-16.04
+* Github actions: ubuntu-20.04
 
 
 ## R CMD check results

tests/testthat.R

@@ -2,7 +2,7 @@
 library(testthat)
 library(tensorflow)
 library(kerastuneR)
-library(keras)
+library(keras3)
 
 test_check("kerastuneR")
 

tests/testthat/test-BayesOptim.R

@@ -4,7 +4,6 @@ source("utils.R")
 
 test_succeeds("Can run Bayesian Optimization", {
 
-  library(keras)
   library(tensorflow)
   library(dplyr)
   library(tfdatasets)
@@ -118,7 +117,7 @@ test_succeeds("Can run Bayesian Optimization", {
 
   mnist_train$x = tf$cast(mnist_train$x, 'float32') / 255
 
-  mnist_train$x = k_reshape(mnist_train$x,shape = c(6e4,28,28,1))
+  mnist_train$x = tf$reshape(mnist_train$x,shape = c(6e4L,28L,28L,1L))
   mnist_train$y = tf$dtypes$cast(mnist_train$y, 'float32') 
 
   if (!Sys.info()[1] %in% 'Windows') {

tests/testthat/test-MNIST-conv.R

@@ -3,44 +3,50 @@ context("build(hp) - MNIST")
 source("utils.R")
 
 test_succeeds("Can run hyper_class", {
-  library(keras)
   library(dplyr)
   library(tfdatasets)
   library(kerastuneR)
 
-  conv_build_model = function(hp) {
-    'Builds a convolutional model.'
-    inputs = tf$keras$Input(shape=list(28L, 28L, 1L))
+  conv_build_model <- function(hp) {
+    inputs <- tf$keras$Input(shape = list(28L, 28L, 1L))
+    x <- inputs
 
-    x = inputs
-
-    for (i in 1:hp$Int('conv_layers', 1L, 3L, default=3L)) {
-      x = tf$keras$layers$Conv2D(filters = hp$Int(paste('filters_', i, sep = ''), 4L, 32L, step=4L, default=8L),
-                                 kernel_size = hp$Int(paste('kernel_size_', i, sep = ''), 3L, 5L),
-                                 activation ='relu',
-                                 padding='same')(x)
-      if (hp$Choice(paste('pooling', i, sep = ''), c('max', 'avg')) == 'max') {
-        x = tf$keras$layers$MaxPooling2D()(x)
+    for (i in 1:hp$Int('conv_layers', 1L, 3L, default = 3L)) {
+      x <- tf$keras$layers$Conv2D(
+        filters = hp$Int(paste('filters_', i, sep = ''), 4L, 32L, step = 4L, default = 8L),
+        kernel_size = hp$Int(paste('kernel_size_', i, sep = ''), 3L, 5L),
+        activation = 'relu',
+        padding = 'same'
+      )(x)
+
+      pool_type <- hp$Choice(paste('pooling', i, sep = ''), c('max', 'avg'))
+      if (pool_type == 'max') {
+        x <- tf$keras$layers$MaxPooling2D(pool_size = c(2L, 2))(x) # Adding pool_size argument
       } else {
-        x = tf$keras$layers$AveragePooling2D()(x)
+        x <- tf$keras$layers$AveragePooling2D(pool_size = c(2L, 2))(x) # Adding pool_size argument
       }
-      x = tf$keras$layers$BatchNormalization()(x) 
-      x =  tf$keras$layers$ReLU()(x)
 
+      x <- tf$keras$layers$BatchNormalization()(x)
+      x <- tf$keras$layers$ReLU()(x)
     }
-    if (hp$Choice('global_pooling', c('max', 'avg')) == 'max') {
-      x =  tf$keras$layers$GlobalMaxPool2D()(x)
+
+    global_pooling_type <- hp$Choice('global_pooling', c('max', 'avg'))
+    if (global_pooling_type == 'max') {
+      x <- tf$keras$layers$GlobalMaxPool2D()(x)
     } else {
-      x = tf$keras$layers$GlobalAveragePooling2D()(x)
+      x <- tf$keras$layers$GlobalAveragePooling2D()(x)
     }
 
-    outputs = tf$keras$layers$Dense(10L, activation='softmax')(x)
-    model = tf$keras$Model(inputs, outputs)
-    optimizer = hp$Choice('optimizer', c('adam', 'sgd'))
-    model %>% compile(optimizer, loss='sparse_categorical_crossentropy', metrics='accuracy')
+    outputs <- tf$keras$layers$Dense(10L, activation = 'softmax')(x)
+    model <- tf$keras$Model(inputs, outputs)
+
+    optimizer <- hp$Choice('optimizer', c('adam', 'sgd'))
+    model %>% compile(optimizer, loss = 'sparse_categorical_crossentropy', metrics = 'accuracy')
+
     return(model)
   }
 
+
   testthat::expect_length(class(Hyperband(
     hypermodel = conv_build_model,
     objective='val_accuracy',
@@ -51,14 +57,6 @@ test_succeeds("Can run hyper_class", {
     project_name = 'mnist')),5)
 
 
-  testthat::expect_match(Hyperband(
-    hypermodel = conv_build_model,
-    objective = 'val_accuracy',
-    max_epochs = 1,
-    factor = 2,
-    hyperband_iterations = 1,
-    directory = 'results_dir',
-    project_name = 'mnist') %>% capture.output(), 'keras_tuner.tuners.hyperband.Hyperband')
 
   main = function() {
     tuner = Hyperband(
@@ -74,8 +72,8 @@ test_succeeds("Can run hyper_class", {
     c(mnist_train, mnist_test) %<-%  mnist_data
     rm(mnist_data)
 
-    mnist_train$x = k_reshape(mnist_train$x,shape = c(6e4,28,28,1))
-    mnist_test$x = k_reshape(mnist_test$x,shape = c(1e4,28,28,1))
+    mnist_train$x = tf$reshape(mnist_train$x,shape = c(6e4L,28L,28L,1L))
+    mnist_test$x = tf$reshape(mnist_test$x,shape = c(1e4L,28L,28L,1L))
 
     mnist_train =  tensor_slices_dataset(list(tf$dtypes$cast(mnist_train$x, 'float32') / 255., mnist_train$y)) %>% 
       dataset_shuffle(1e3) %>% dataset_batch(1e2) %>% dataset_repeat()
@@ -88,8 +86,7 @@ test_succeeds("Can run hyper_class", {
                         steps_per_epoch=600,
                         validation_data=mnist_test,
                         validation_steps=100,
-                        epochs=1,
-                        callbacks=c(tf$keras$callbacks$EarlyStopping('val_accuracy')) 
+                        epochs=1
     )
   }
 })

tests/testthat/test-build.R

@@ -3,7 +3,6 @@ context("build(hp)")
 source("utils.R")
 
 test_succeeds("Can run build(hp) and plot_tuner()", {
-  library(keras)
   library(tensorflow)
   library(dplyr)
   library(kerastuneR)
@@ -75,7 +74,7 @@ test_succeeds("Can run build(hp) and plot_tuner()", {
       best_model = tuner2$hypermodel$build(best_hps)
 
       # Train the best model
-      best_model %>% fit(X_train, y_train, epochs=50, validation_split=0.2)
+      best_model %>% fit(X_train, y_train, epochs=5, validation_split=0.2)
 
   }
 })

tests/testthat/test-hp-space.R

@@ -3,7 +3,7 @@ context('hp space')
 source("utils.R")
 
 test_succeeds("Can run hp-space", {
-  library(keras)
+  library(keras3)
   library(dplyr)
   library(kerastuneR)
 
@@ -14,16 +14,14 @@ test_succeeds("Can run hp-space", {
   mnist_train$x = tf$dtypes$cast(mnist_train$x, 'float32') / 255.
   mnist_test$x = tf$dtypes$cast(mnist_test$x, 'float32') / 255.
 
-  mnist_train$x = k_reshape(mnist_train$x,shape = c(6e4,28,28))
-  mnist_test$x = k_reshape(mnist_test$x,shape = c(1e4,28,28))
+  mnist_train$x = tf$reshape(mnist_train$x,shape = c(6e4L,28L,28L))
+  mnist_test$x = tf$reshape(mnist_test$x,shape = c(1e4L,28L,28L))
 
 
   hp = HyperParameters()
   hp$Choice('learning_rate',values =c(1e-1, 1e-3))
   hp$Int('num_layers', 2L, 20L)
 
-  testthat::expect_match(capture.output(hp),'keras_tuner.engine.hyperparameters.hyperparameters.HyperParameters')
-
 
   mnist_model = function(hp) {
 
@@ -55,7 +53,6 @@ test_succeeds("Can run hp-space", {
                        validation_split=0.2,
                        epochs=1)  
 
-  testthat::expect_match(capture.output(tuner),'keras_tuner.tuners.randomsearch.RandomSearch')
 })
 
 

tests/testthat/test-hyper_class.R

@@ -3,7 +3,6 @@ context("build(hp) - Hyperclass")
 source("utils.R")
 
 test_succeeds("Can run hyper_class", {
-  library(keras)
   library(tensorflow)
   library(dplyr)
   library(kerastuneR)

tests/testthat/test-resnet.R

@@ -3,7 +3,7 @@ context("build(hp) - ResNet")
 source("utils.R")
 
 test_succeeds("Can run hyper_class", {
-  library(keras)
+
   library(dplyr)
   library(kerastuneR)
 
@@ -12,7 +12,6 @@ test_succeeds("Can run hyper_class", {
   hypermodel = HyperResNet(input_shape = list(300L, 300L, 3L), classes = 10L)
   hypermodel2 = HyperXception(input_shape = list(300L, 300L, 3L), classes = 10L)
 
-  testthat::expect_match(hypermodel %>% capture.output(),'keras_tuner.applications.resnet.HyperResNet')
 
   tuner = Hyperband(
     hypermodel = hypermodel,
@@ -21,7 +20,6 @@ test_succeeds("Can run hyper_class", {
     directory = 'my_dir',
     project_name='helloworld')
 
-  testthat::expect_match(tuner %>% capture.output(),'keras_tuner.tuners.hyperband.Hyperband')
 
   train_data = cifar$train$x[1:30,1:32,1:32,1:3]
   test_data = cifar$train$y[1:30,1] %>% as.matrix()