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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 )
Used in the notebooks
Used in the guide | Used in the tutorials |
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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]]
keras.utils.pad_sequences(sequence)
array([[0, 0, 1],
[0, 2, 3],
[4, 5, 6]], dtype=int32)
keras.utils.pad_sequences(sequence, value=-1)
array([[-1, -1, 1],
[-1, 2, 3],
[ 4, 5, 6]], dtype=int32)
keras.utils.pad_sequences(sequence, padding='post')
array([[1, 0, 0],
[2, 3, 0],
[4, 5, 6]], dtype=int32)
keras.utils.pad_sequences(sequence, maxlen=2)
array([[0, 1],
[2, 3],
[5, 6]], dtype=int32)
Returns | |
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NumPy array with shape (len(sequences), maxlen) |