diff --git a/tests/testthat.R b/tests/testthat.R
index b61ac4d..7066df2 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -2,7 +2,7 @@
 library(testthat)
 library(tensorflow)
 library(kerastuneR)
-library(keras)
+library(keras3)
 
 test_check("kerastuneR")
 
diff --git a/tests/testthat/test-BayesOptim.R b/tests/testthat/test-BayesOptim.R
index dc4e674..4615987 100644
--- a/tests/testthat/test-BayesOptim.R
+++ b/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') {
diff --git a/tests/testthat/test-MNIST-conv.R b/tests/testthat/test-MNIST-conv.R
index 270f24e..a41dfdf 100644
--- a/tests/testthat/test-MNIST-conv.R
+++ b/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
     )
   }
 })
diff --git a/tests/testthat/test-build.R b/tests/testthat/test-build.R
index 8ee1e6f..8db7552 100644
--- a/tests/testthat/test-build.R
+++ b/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)
     
   }
 })
diff --git a/tests/testthat/test-hp-space.R b/tests/testthat/test-hp-space.R
index 85752dc..f1d363d 100644
--- a/tests/testthat/test-hp-space.R
+++ b/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)
   
@@ -22,8 +22,6 @@ test_succeeds("Can run hp-space", {
   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) {
     
diff --git a/tests/testthat/test-hyper_class.R b/tests/testthat/test-hyper_class.R
index bdb6027..cc2548d 100644
--- a/tests/testthat/test-hyper_class.R
+++ b/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)