Reuters newswire topics classification

Dataset of 11,228 newswires from Reuters, labeled over 46 topics. As with dataset_imdb() , each wire is encoded as a sequence of word indexes (same conventions).

Usage

dataset_reuters(
  path = "reuters.npz",
  num_words = NULL,
  skip_top = 0L,
  maxlen = NULL,
  test_split = 0.2,
  seed = 113L,
  start_char = 1L,
  oov_char = 2L,
  index_from = 3L,
  convert = TRUE
)

dataset_reuters_word_index(path = "reuters_word_index.pkl")

Arguments

path

Where to cache the data (relative to ~/.keras/dataset).

num_words

Max number of words to include. Words are ranked by how often they occur (in the training set) and only the most frequent words are kept

skip_top

Skip the top N most frequently occuring words (which may not be informative).

maxlen

Truncate sequences after this length.

test_split

Fraction of the dataset to be used as test data.

seed

Random seed for sample shuffling.

start_char

The start of a sequence will be marked with this character. Set to 1 because 0 is usually the padding character.

oov_char

words that were cut out because of the num_words or skip_top limit will be replaced with this character.

index_from

index actual words with this index and higher.

convert

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

Value

Lists of training and test data: train$x, train$y, test$x, test$y with same format as dataset_imdb(). The dataset_reuters_word_index() function returns a list where the names are words and the values are integer. e.g. word_index[["giraffe"]] might return 1234.

train/
├─ x
└─ y
test/
├─ x
└─ y

str(dataset_reuters())

## List of 2
##  $ train:List of 2
##   ..$ x:List of 8982
##   .. ..$ : int [1:87] 1 27595 28842 8 43 10 447 5 25 207 ...
##   .. ..$ : int [1:56] 1 3267 699 3434 2295 56 16784 7511 9 56 ...
##   .. ..$ : int [1:139] 1 53 12 284 15 14 272 26 53 959 ...
##   .. ..$ : int [1:224] 1 4 686 867 558 4 37 38 309 2276 ...
##   .. ..$ : int [1:101] 1 8295 111 8 25 166 40 638 10 436 ...
##   .. ..$ : int [1:116] 1 4 37 38 309 213 349 1632 48 193 ...
##   .. .. [list output truncated]
##   ..$ y: int [1:8982] 3 4 3 4 4 4 4 3 3 16 ...
##  $ test :List of 2
##   ..$ x:List of 2246
##   .. ..$ : int [1:145] 1 4 1378 2025 9 697 4622 111 8 25 ...
##   .. ..$ : int [1:745] 1 2768 283 122 7 4 89 544 463 29 ...
##   .. ..$ : int [1:228] 1 4 309 2276 4759 5 2015 403 1920 33 ...
##   .. ..$ : int [1:172] 1 11786 13716 65 9 249 1096 8 16 515 ...
##   .. ..$ : int [1:187] 1 470 354 18270 4231 62 2373 509 1687 5138 ...
##   .. ..$ : int [1:80] 1 53 134 26 14 102 26 39 5150 18 ...
##   .. .. [list output truncated]
##   ..$ y: int [1:2246] 3 10 1 4 4 3 3 3 3 3 ...

str(dataset_reuters(convert = FALSE))

## List of 2
##  $ train:List of 2
##   ..$ x: <numpy.ndarray shape(8982), dtype=object>
##   ..$ y: <numpy.ndarray shape(8982), dtype=int64>
##  $ test :List of 2
##   ..$ x: <numpy.ndarray shape(2246), dtype=object>
##   ..$ y: <numpy.ndarray shape(2246), dtype=int64>

See also

Other datasets:
dataset_boston_housing()
dataset_california_housing()
dataset_cifar10()
dataset_cifar100()
dataset_fashion_mnist()
dataset_imdb()
dataset_mnist()