pad_sequences

keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype='int32',
    padding='pre', truncating='pre', value=0.)

Transform a list of nb_samples sequences (lists of scalars) into a 2D Numpy array of shape (nb_samples, nb_timesteps). nb_timesteps is either the maxlen argument if provided, or the length of the longest sequence otherwise. Sequences that are shorter than nb_timesteps are padded with value at the end. Sequences longer than nb_timesteps are truncated so that it fits the desired length. Position where padding or truncation happens is determined by padding or truncating, respectively.

  • Return: 2D Numpy array of shape (nb_samples, nb_timesteps).

  • Arguments:

    • sequences: List of lists of int or float.
    • maxlen: None or int. Maximum sequence length, longer sequences are truncated and shorter sequences are padded with zeros at the end.
    • dtype: datatype of the Numpy array returned.
    • padding: 'pre' or 'post', pad either before or after each sequence.
    • truncating: 'pre' or 'post', remove values from sequences larger than maxlen either in the beginning or in the end of the sequence
    • value: float, value to pad the sequences to the desired value.

skipgrams

keras.preprocessing.sequence.skipgrams(sequence, vocabulary_size,
    window_size=4, negative_samples=1., shuffle=True,
    categorical=False, sampling_table=None)

Transforms a sequence of word indexes (list of int) into couples of the form:

  • (word, word in the same window), with label 1 (positive samples).
  • (word, random word from the vocabulary), with label 0 (negative samples).

Read more about Skipgram in this gnomic paper by Mikolov et al.: Efficient Estimation of Word Representations in Vector Space

  • Return: tuple (couples, labels).

    • couples is a list of 2-elements lists of int: [word_index, other_word_index].
    • labels is a list of 0 and 1, where 1 indicates that other_word_index was found in the same window as word_index, and 0 indicates that other_word_index was random.
    • if categorical is set to True, the labels are categorical, ie. 1 becomes [0,1], and 0 becomes [1, 0].

  • Arguments:

    • sequence: list of int indexes. If using a sampling_table, the index of a word should be its the rank in the dataset (starting at 1).
    • vocabulary_size: int.
    • window_size: int. maximum distance between two words in a positive couple.
    • negative_samples: float >= 0. 0 for no negative (=random) samples. 1 for same number as positive samples. etc.
    • shuffle: boolean. Whether to shuffle the samples.
    • categorical: boolean. Whether to make the returned labels categorical.
    • sampling_table: Numpy array of shape (vocabulary_size,) where sampling_table[i] is the probability of sampling the word with index i (assumed to be i-th most common word in the dataset).

make_sampling_table

keras.preprocessing.sequence.make_sampling_table(size, sampling_factor=1e-5)

Used for generating the sampling_table argument for skipgrams. sampling_table[i] is the probability of sampling the word i-th most common word in a dataset (more common words should be sampled less frequently, for balance).

  • Return: Numpy array of shape (size,).

  • Arguments:

    • size: size of the vocabulary considered.
    • sampling_factor: lower values result in a longer probability decay (common words will be sampled less frequently). If set to 1, no subsampling will be performed (all sampling probabilities will be 1).