Dataset of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images.
dataset_cifar10(convert = TRUE)Lists of training and test data: train$x, train$y, test$x, test$y.
str(dataset_cifar10())
## List of 2
## $ train:List of 2
## ..$ x: int [1:50000, 1:32, 1:32, 1:3] 59 154 255 28 170 159 164 28 134 125 ...
## ..$ y: int [1:50000, 1] 6 9 9 4 1 1 2 7 8 3 ...
## $ test :List of 2
## ..$ x: int [1:10000, 1:32, 1:32, 1:3] 158 235 158 155 65 179 160 83 23 217 ...
## ..$ y: int [1:10000, 1] 3 8 8 0 6 6 1 6 3 1 ...str(dataset_cifar10(convert = FALSE))
## List of 2
## $ train:List of 2
## ..$ x: <numpy.ndarray shape(50000,32,32,3), dtype=uint8>
## ..$ y: <numpy.ndarray shape(50000,1), dtype=uint8>
## $ test :List of 2
## ..$ x: <numpy.ndarray shape(10000,32,32,3), dtype=uint8>
## ..$ y: <numpy.ndarray shape(10000,1), dtype=uint8>The x data is an array of RGB image data with shape (num_samples, 3, 32,
32).
The y data is an array of category labels (integers in range 0-9) with
shape (num_samples).
When TRUE (default) the datasets are returned as R arrays.
If FALSE, objects are returned as NumPy arrays.
Other datasets:
dataset_boston_housing()
dataset_california_housing()
dataset_cifar100()
dataset_fashion_mnist()
dataset_imdb()
dataset_mnist()
dataset_reuters()