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Improves documentation for keras.preprocessing.pad_sequence

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PiperOrigin-RevId: 297247197
Change-Id: I4c85e8ba6d4ae43d4c249442ef9c47bb6a5805c6
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A. Unique TensorFlower 2020-02-25 17:50:37 -08:00 committed by TensorFlower Gardener
parent 42e6af1ac7
commit 82bcbf0560
1 changed files with 69 additions and 2 deletions
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71
tensorflow/python/keras/preprocessing/sequence.py
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@ -24,7 +24,6 @@ from keras_preprocessing import sequence
from tensorflow.python.keras.utils import data_utils
from tensorflow.python.util.tf_export import keras_export
pad_sequences = sequence.pad_sequences
make_sampling_table = sequence.make_sampling_table
skipgrams = sequence.skipgrams
# TODO(fchollet): consider making `_remove_long_seq` public.
@ -34,6 +33,7 @@ _remove_long_seq = sequence._remove_long_seq # pylint: disable=protected-access
@keras_export('keras.preprocessing.sequence.TimeseriesGenerator')
class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):
"""Utility class for generating batches of temporal data.
This class takes in a sequence of data-points gathered at
equal intervals, along with time series parameters such as
stride, length of history, etc., to produce batches for
@ -89,7 +89,74 @@ class TimeseriesGenerator(sequence.TimeseriesGenerator, data_utils.Sequence):
pass
keras_export('keras.preprocessing.sequence.pad_sequences')(pad_sequences)
@keras_export('keras.preprocessing.sequence.pad_sequences')
def pad_sequences(sequences, maxlen=None, dtype='int32',
padding='pre', truncating='pre', value=0.):
"""Pads sequences to the same length.
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.preprocessing.sequence.pad_sequences(sequence)
array([[0, 0, 1],
[0, 2, 3],
[4, 5, 6]], dtype=int32)
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence, value=-1)
array([[-1, -1, 1],
[-1, 2, 3],
[ 4, 5, 6]], dtype=int32)
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence, padding='post')
array([[1, 0, 0],
[2, 3, 0],
[4, 5, 6]], dtype=int32)
>>> tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=2)
array([[0, 1],
[2, 3],
[5, 6]], dtype=int32)
Arguments:
sequences: List of sequences (each sequence is a list of integers).
maxlen: Optional Int, maximum length of all sequences. If not provided,
sequences will be padded to the length of the longest individual
sequence.
dtype: (Optional, defaults to int32). Type of the output sequences.
To pad sequences with variable length strings, you can use `object`.
padding: String, 'pre' or 'post' (optional, defaults to 'pre'):
pad either before or after each sequence.
truncating: String, 'pre' or 'post' (optional, defaults to 'pre'):
remove values from sequences larger than
`maxlen`, either at the beginning or at the end of the sequences.
value: Float or String, padding value. (Optional, defaults to 0.)
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.
"""
return sequence.pad_sequences(
sequences, maxlen=maxlen, dtype=dtype,
padding=padding, truncating=truncating, value=value)
keras_export(
'keras.preprocessing.sequence.make_sampling_table')(make_sampling_table)
keras_export('keras.preprocessing.sequence.skipgrams')(skipgrams)