keras3/man/dataset_imdb.Rd at a14bd68e2bf750e6ed74c808854bf7cda4ed759f · rstudio/keras3 · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\name{dataset_imdb}
\alias{dataset_imdb}
\alias{dataset_imdb_word_index}
\title{IMDB Movie reviews sentiment classification}
\usage{
dataset_imdb(
  path = "imdb.npz",
  num_words = NULL,
  skip_top = 0L,
  maxlen = NULL,
  seed = 113L,
  start_char = 1L,
  oov_char = 2L,
  index_from = 3L,
  convert = TRUE
)

dataset_imdb_word_index(path = "imdb_word_index.json")
}
\arguments{
\item{path}{Where to cache the data (relative to \verb{~/.keras/dataset}).}

\item{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}

\item{skip_top}{Skip the top N most frequently occuring words (which may not
be informative).}

\item{maxlen}{sequences longer than this will be filtered out.}

\item{seed}{random seed for sample shuffling.}

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

\item{oov_char}{Words that were cut out because of the \code{num_words} or
\code{skip_top} limit will be replaced with this character.}

\item{index_from}{Index actual words with this index and higher.}

\item{convert}{When \code{TRUE} (default) the datasets are returned as R arrays.
If \code{FALSE}, objects are returned as NumPy arrays.}
}
\value{
Lists of training and test data: \verb{train$x, train$y, test$x, test$y}.

\if{html}{\out{<div class="sourceCode">}}\preformatted{train/
├─ x
└─ y
test/
├─ x
└─ y
}\if{html}{\out{</div>}}

The \code{x} data includes integer sequences. If the \code{num_words} argument was
specific, the maximum possible index value is \code{num_words-1}. If the
\code{maxlen} argument was specified, the largest possible sequence length is
\code{maxlen}.

The \code{y} data includes a set of integer labels (0 or 1).

\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_imdb())
}\if{html}{\out{</div>}}

\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
##  $ train:List of 2
##   ..$ x:List of 25000
##   .. ..$ : int [1:218] 1 14 22 16 43 530 973 1622 1385 65 ...
##   .. ..$ : int [1:189] 1 194 1153 194 8255 78 228 5 6 1463 ...
##   .. ..$ : int [1:141] 1 14 47 8 30 31 7 4 249 108 ...
##   .. ..$ : int [1:550] 1 4 18609 16085 33 2804 4 2040 432 111 ...
##   .. ..$ : int [1:147] 1 249 1323 7 61 113 10 10 13 1637 ...
##   .. ..$ : int [1:43] 1 778 128 74 12 630 163 15 4 1766 ...
##   .. .. [list output truncated]
##   ..$ y: int [1:25000] 1 0 0 1 0 0 1 0 1 0 ...
##  $ test :List of 2
##   ..$ x:List of 25000
##   .. ..$ : int [1:68] 1 591 202 14 31 6 717 10 10 18142 ...
##   .. ..$ : int [1:260] 1 14 22 3443 6 176 7 5063 88 12 ...
##   .. ..$ : int [1:603] 1 111 748 4368 1133 33782 24563 4 87 1551 ...
##   .. ..$ : int [1:181] 1 13 1228 119 14 552 7 20 190 14 ...
##   .. ..$ : int [1:108] 1 40 49 85 84 1040 146 6 783 254 ...
##   .. ..$ : int [1:132] 1 146 427 5718 14 20 218 112 2962 32 ...
##   .. .. [list output truncated]
##   ..$ y: int [1:25000] 0 1 1 0 1 1 1 0 0 1 ...

}\if{html}{\out{</div>}}

\if{html}{\out{<div class="sourceCode r">}}\preformatted{str(dataset_imdb(convert = FALSE))
}\if{html}{\out{</div>}}

\if{html}{\out{<div class="sourceCode">}}\preformatted{## List of 2
##  $ train:List of 2
##   ..$ x: <numpy.ndarray shape(25000), dtype=object>
##   ..$ y: <numpy.ndarray shape(25000), dtype=int64>
##  $ test :List of 2
##   ..$ x: <numpy.ndarray shape(25000), dtype=object>
##   ..$ y: <numpy.ndarray shape(25000), dtype=int64>

}\if{html}{\out{</div>}}

The \code{dataset_imdb_word_index()} function returns a list where the
names are words and the values are integer.
}
\description{
Dataset of 25,000 movies reviews from IMDB, labeled by sentiment
(positive/negative). Reviews have been preprocessed, and each review is
encoded as a sequence of word indexes (integers). For convenience, words are
indexed by overall frequency in the dataset, so that for instance the integer
"3" encodes the 3rd most frequent word in the data. This allows for quick
filtering operations such as: "only consider the top 10,000 most common
words, but eliminate the top 20 most common words".
}
\details{
As a convention, "0" does not stand for a specific word, but instead is used
to encode any unknown word.
}
\seealso{
Other datasets: \cr
\code{\link{dataset_boston_housing}()} \cr
\code{\link{dataset_california_housing}()} \cr
\code{\link{dataset_cifar10}()} \cr
\code{\link{dataset_cifar100}()} \cr
\code{\link{dataset_fashion_mnist}()} \cr
\code{\link{dataset_mnist}()} \cr
\code{\link{dataset_reuters}()} \cr
}
\concept{datasets}