tf.keras.preprocessing.sequence.pad_sequences
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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.
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.preprocessing.sequence.pad_sequences\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences) |\n\nPads sequences to the same length.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.preprocessing.sequence.pad_sequences`](/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences)\n\n\u003cbr /\u003e\n\n tf.keras.preprocessing.sequence.pad_sequences(\n sequences, maxlen=None, dtype='int32', padding='pre', truncating='pre',\n value=0.0\n )\n\nThis function transforms a list of\n`num_samples` sequences (lists of integers)\ninto a 2D Numpy array of shape `(num_samples, num_timesteps)`.\n`num_timesteps` is either the `maxlen` argument if provided,\nor the length of the longest sequence otherwise.\n\nSequences that are shorter than `num_timesteps`\nare padded with `value` at the beginning or the end\nif padding='post.\n\nSequences longer than `num_timesteps` are truncated\nso that they fit the desired length.\nThe position where padding or truncation happens is determined by\nthe arguments `padding` and `truncating`, respectively.\n\nPre-padding is the default.\n\nArguments\n=========\n\n sequences: List of lists, where each element is a sequence.\n maxlen: Int, maximum length of all sequences.\n dtype: Type of the output sequences.\n To pad sequences with variable length strings, you can use `object`.\n padding: String, 'pre' or 'post':\n pad either before or after each sequence.\n truncating: String, 'pre' or 'post':\n remove values from sequences larger than\n `maxlen`, either at the beginning or at the end of the sequences.\n value: Float or String, padding value.\n\nReturns\n=======\n\n x: Numpy array with shape `(len(sequences), maxlen)`\n\nRaises\n======\n\n ValueError: In case of invalid values for `truncating` or `padding`,\n or in case of invalid shape for a `sequences` entry."]]
TensorFlow 1 version