This function transforms a list (of length num_samples
)
of sequences (lists of integers)
into a 2D NumPy array of shape (num_samples, num_timesteps)
.
num_timesteps
is either the maxlen
argument if provided,
or the length of the longest sequence in the list.
Sequences that are shorter than num_timesteps
are padded with value
until they are num_timesteps
long.
Sequences longer than num_timesteps
are truncated
so that they fit the desired length.
The position where padding or truncation happens is determined by
the arguments padding
and truncating
, respectively.
Pre-padding or removing values from the beginning of the sequence is the
default.
sequence <- list(c(1), c(2, 3), c(4, 5, 6))
pad_sequences(sequence)
pad_sequences(sequence, value=-1)
pad_sequences(sequence, padding='post')
pad_sequences(sequence, maxlen=2)
Usage
pad_sequences(
sequences,
maxlen = NULL,
dtype = "int32",
padding = "pre",
truncating = "pre",
value = 0
)
Arguments
- sequences
List of sequences (each sequence is a list of integers).
- maxlen
Optional Int, maximum length of all sequences. If not provided, sequences will be padded to the length of the longest individual sequence.
- dtype
(Optional, defaults to
"int32"
). Type of the output sequences. To pad sequences with variable length strings, you can useobject
.- padding
String, "pre" or "post" (optional, defaults to
"pre"
): pad either before or after each sequence.- truncating
String, "pre" or "post" (optional, defaults to
"pre"
): remove values from sequences larger thanmaxlen
, either at the beginning or at the end of the sequences.- value
Float or String, padding value. (Optional, defaults to
0
)
See also
Other utils: audio_dataset_from_directory()
clear_session()
config_disable_interactive_logging()
config_disable_traceback_filtering()
config_enable_interactive_logging()
config_enable_traceback_filtering()
config_is_interactive_logging_enabled()
config_is_traceback_filtering_enabled()
get_file()
get_source_inputs()
image_array_save()
image_dataset_from_directory()
image_from_array()
image_load()
image_smart_resize()
image_to_array()
layer_feature_space()
normalize()
set_random_seed()
split_dataset()
text_dataset_from_directory()
timeseries_dataset_from_array()
to_categorical()
zip_lists()