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import numpy as np |
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from keras.src.api_export import keras_export |
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@keras_export( |
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[ |
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"keras.utils.pad_sequences", |
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"keras.preprocessing.sequence.pad_sequences", |
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] |
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) |
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def pad_sequences( |
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sequences, |
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maxlen=None, |
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dtype="int32", |
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padding="pre", |
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truncating="pre", |
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value=0.0, |
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): |
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"""Pads sequences to the same length. |
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This function transforms a list (of length `num_samples`) |
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of sequences (lists of integers) |
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into a 2D NumPy array of shape `(num_samples, num_timesteps)`. |
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`num_timesteps` is either the `maxlen` argument if provided, |
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or the length of the longest sequence in the list. |
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Sequences that are shorter than `num_timesteps` |
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are padded with `value` until they are `num_timesteps` long. |
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Sequences longer than `num_timesteps` are truncated |
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so that they fit the desired length. |
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The position where padding or truncation happens is determined by |
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the arguments `padding` and `truncating`, respectively. |
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Pre-padding or removing values from the beginning of the sequence is the |
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default. |
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>>> sequence = [[1], [2, 3], [4, 5, 6]] |
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>>> keras.utils.pad_sequences(sequence) |
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array([[0, 0, 1], |
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[0, 2, 3], |
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[4, 5, 6]], dtype=int32) |
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>>> keras.utils.pad_sequences(sequence, value=-1) |
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array([[-1, -1, 1], |
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[-1, 2, 3], |
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[ 4, 5, 6]], dtype=int32) |
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>>> keras.utils.pad_sequences(sequence, padding='post') |
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array([[1, 0, 0], |
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[2, 3, 0], |
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[4, 5, 6]], dtype=int32) |
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>>> keras.utils.pad_sequences(sequence, maxlen=2) |
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array([[0, 1], |
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[2, 3], |
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[5, 6]], dtype=int32) |
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Args: |
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sequences: List of sequences (each sequence is a list of integers). |
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maxlen: Optional Int, maximum length of all sequences. If not provided, |
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sequences will be padded to the length of the longest individual |
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sequence. |
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dtype: (Optional, defaults to `"int32"`). Type of the output sequences. |
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To pad sequences with variable length strings, you can use `object`. |
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padding: String, "pre" or "post" (optional, defaults to `"pre"`): |
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pad either before or after each sequence. |
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truncating: String, "pre" or "post" (optional, defaults to `"pre"`): |
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remove values from sequences larger than |
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`maxlen`, either at the beginning or at the end of the sequences. |
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value: Float or String, padding value. (Optional, defaults to `0.`) |
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Returns: |
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NumPy array with shape `(len(sequences), maxlen)` |
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""" |
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if not hasattr(sequences, "__len__"): |
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raise ValueError("`sequences` must be iterable.") |
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num_samples = len(sequences) |
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lengths = [] |
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sample_shape = () |
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flag = True |
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for x in sequences: |
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try: |
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lengths.append(len(x)) |
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if flag and len(x): |
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sample_shape = np.asarray(x).shape[1:] |
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flag = False |
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except TypeError as e: |
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raise ValueError( |
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"`sequences` must be a list of iterables. " |
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f"Found non-iterable: {str(x)}" |
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) from e |
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if maxlen is None: |
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maxlen = np.max(lengths) |
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is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype( |
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dtype, np.str_ |
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) |
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if isinstance(value, str) and dtype is not object and not is_dtype_str: |
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raise ValueError( |
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f"`dtype` {dtype} is not compatible with `value`'s type: " |
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f"{type(value)}\nYou should set `dtype=object` for variable length " |
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"strings." |
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) |
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x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype) |
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for idx, s in enumerate(sequences): |
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if not len(s): |
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continue |
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if truncating == "pre": |
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trunc = s[-maxlen:] |
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elif truncating == "post": |
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trunc = s[:maxlen] |
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else: |
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raise ValueError(f'Truncating type "{truncating}" not understood') |
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trunc = np.asarray(trunc, dtype=dtype) |
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if trunc.shape[1:] != sample_shape: |
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raise ValueError( |
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f"Shape of sample {trunc.shape[1:]} of sequence at " |
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f"position {idx} is different from expected shape " |
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f"{sample_shape}" |
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) |
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if padding == "post": |
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x[idx, : len(trunc)] = trunc |
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elif padding == "pre": |
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x[idx, -len(trunc) :] = trunc |
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else: |
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raise ValueError(f'Padding type "{padding}" not understood') |
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return x |
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