TensorFlow 2 version
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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.
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.