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()