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 2elements lists of int:[word_index, other_word_index]
.labels
is a list of 0 and 1, where 1 indicates thatother_word_index
was found in the same window asword_index
, and 0 indicates thatother_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,)
wheresampling_table[i]
is the probability of sampling the word with index i (assumed to be ith most common word in the dataset).
make_sampling_table
keras.preprocessing.sequence.make_sampling_table(size, sampling_factor=1e5)
Used for generating the sampling_table
argument for skipgrams
. sampling_table[i]
is the probability of sampling the word ith 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).