tf.keras.preprocessing.sequence.pad_sequences

Pads sequences to the same length.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.preprocessing.sequence.pad_sequences, `tf.compat.v2.keras.preprocessing.sequence.pad_sequences`

tf.keras.preprocessing.sequence.pad_sequences(
    sequences, maxlen=None, dtype='int32', padding='pre', truncating='pre',
    value=0.0
)

This function transforms a list of num_samples 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 otherwise.

Sequences that are shorter than num_timesteps are padded with value at the beginning or the end if padding='post.

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 is the default.

Arguments

sequences: List of lists, where each element is a sequence.
maxlen: Int, maximum length of all sequences.
dtype: Type of the output sequences.
    To pad sequences with variable length strings, you can use `object`.
padding: String, 'pre' or 'post':
    pad either before or after each sequence.
truncating: String, 'pre' or 'post':
    remove values from sequences larger than
    `maxlen`, either at the beginning or at the end of the sequences.
value: Float or String, padding value.

Returns

x: 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.