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Pads sequences to the same length.
tf.keras.utils.pad_sequences(
sequences,
maxlen=None,
dtype='int32',
padding='pre',
truncating='pre',
value=0.0
)
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 = [[1], [2, 3], [4, 5, 6]]
tf.keras.utils.pad_sequences(sequence)
array([[0, 0, 1],
[0, 2, 3],
[4, 5, 6]], dtype=int32)
tf.keras.utils.pad_sequences(sequence, value=-1)
array([[-1, -1, 1],
[-1, 2, 3],
[ 4, 5, 6]], dtype=int32)
tf.keras.utils.pad_sequences(sequence, padding='post')
array([[1, 0, 0],
[2, 3, 0],
[4, 5, 6]], dtype=int32)
tf.keras.utils.pad_sequences(sequence, maxlen=2)
array([[0, 1],
[2, 3],
[5, 6]], dtype=int32)
Returns | |
---|---|
Numpy array with shape (len(sequences), maxlen)
|
Raises | |
---|---|
ValueError
|
In case of invalid values for truncating or padding ,
or in case of invalid shape for a sequences entry.
|