keras3 (version 1.5.0)

dataset_cifar100: CIFAR100 small image classification

Description

Dataset of 50,000 32x32 color training images, labeled over 100 categories, and 10,000 test images.

Usage

dataset_cifar100(label_mode = c("fine", "coarse"), convert = TRUE)

Value

Lists of training and test data: train$x, train$y, test$x, test$y.

str(dataset_cifar100())

## List of 2
##  $ train:List of 2
##   ..$ x: int [1:50000, 1:32, 1:32, 1:3] 255 255 250 124 43 190 50 178 122 255 ...
##   ..$ y: num [1:50000, 1] 19 29 0 11 1 86 90 28 23 31 ...
##  $ test :List of 2
##   ..$ x: int [1:10000, 1:32, 1:32, 1:3] 199 113 61 93 80 168 37 175 233 182 ...
##   ..$ y: num [1:10000, 1] 49 33 72 51 71 92 15 14 23 0 ...

str(dataset_cifar100(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=int64>
##  $ test :List of 2
##   ..$ x: <numpy.ndarray shape(10000,32,32,3), dtype=uint8>
##   ..$ y: <numpy.ndarray shape(10000,1), dtype=int64>

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 with shape (num_samples).

Arguments

label_mode

one of "fine", "coarse".

convert

When TRUE (default) the datasets are returned as R arrays. If FALSE, objects are returned as NumPy arrays.

See Also

Other datasets:
dataset_boston_housing()
dataset_california_housing()
dataset_cifar10()
dataset_fashion_mnist()
dataset_imdb()
dataset_mnist()
dataset_reuters()