Datasets: Load datasets

In kerasR: R Interface to the Keras Deep Learning Library

Load datasets

Description

These functions all return a named list with elements X_train, X_test, Y_train, and Y_test. The first time calling this function will download the datasets locally; thereafter they will be loaded from the keras cache directory.

Usage

load_cifar10()

load_cifar100(label_mode = "fine")

load_imdb(num_words = NULL, skip_top = 0, maxlen = NULL, seed = 113,
  start_char = 1, oov_char = 2, index_from = 3)

load_reuters(num_words = NULL, skip_top = 0, maxlen = 1000,
  test_split = 0.2, seed = 113, start_char = 1, oov_char = 2,
  index_from = 3)

load_mnist()

load_boston_housing()

Arguments

label_mode	either "fine" or "coarse"; how to construct labels for load_cifar100.
num_words	integer or NULL. Top most frequent words to consider. Any less frequent word will appear as 0 in the sequence data.
skip_top	integer. Top most frequent words to ignore (they will appear as 0s in the sequence data).
maxlen	integer. Maximum sequence length. Any longer sequence will be truncated.
seed	integer. Seed for reproducible data shuffling.
start_char	integer. The start of a sequence will be marked with this character. Set to 1 because 0 is usually the padding character.
oov_char	integer. words that were cut out because of the num_words or skip_top limit will be replaced with this character.
index_from	integer. Index actual words with this index and higher.
test_split	float. Fraction of the dataset to use for testing.

Author(s)

Taylor B. Arnold, [e-mail]

References

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

Examples

if (keras_available()) {
  boston <- load_boston_housing()
  X_train <- normalize(boston$X_train, 0)
  Y_train <- boston$Y_train
  X_test <- normalize(boston$X_test, 0)
  Y_test <- boston$Y_test
  
  mod <- Sequential()
  mod$add(Dense(units = 200, input_shape = 13))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 200))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 1))
  keras_compile(mod,  loss = 'mse', optimizer = SGD())
  
  keras_fit(mod, scale(X_train), Y_train,
            batch_size = 32, epochs = 20,
            verbose = 1, validation_split = 0.1)
}

kerasR documentation built on Aug. 17, 2022, 5:06 p.m.