Nothing
context('hp space')
source("utils.R")
test_succeeds("Can run hp-space", {
library(keras)
library(dplyr)
library(kerastuneR)
mnist_data = dataset_fashion_mnist()
c(mnist_train, mnist_test) %<-% mnist_data
rm(mnist_data)
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))
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) {
model = keras_model_sequential() %>%
layer_flatten(input_shape = c(28,28))
for (i in 1:(hp$get('num_layers')) ) {
model %>% layer_dense(32, activation='relu') %>%
layer_dense(units = 10, activation='softmax')
} %>%
compile(
optimizer = tf$keras$optimizers$Adam(hp$get('learning_rate')),
loss = 'sparse_categorical_crossentropy',
metrics = 'accuracy')
return(model)
}
tuner = RandomSearch(
hypermodel = mnist_model,
max_trials=5,
hyperparameters=hp,
tune_new_entries=T,
objective='val_accuracy',
directory='my_dir4',
project_name = 'mnist_')
tuner %>% fit_tuner(mnist_train$x, mnist_train$y,
validation_split=0.2,
epochs=1)
testthat::expect_match(capture.output(tuner),'keras_tuner.tuners.randomsearch.RandomSearch')
})
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