Pads sequences to the same length.

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.

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.

x: Numpy array with shape `(len(sequences), maxlen)`

ValueError: In case of invalid values for `truncating` or `padding`, or in case of invalid shape for a `sequences` entry.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences