keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype='int32')
Transform a list of
nb_samples sequences (lists of scalars) into a 2D Numpy array of shape
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 zeros at the end.
Return: 2D Numpy array of shape
- 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.
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
couplesis a list of 2-elements lists of int:
labelsis a list of 0 and 1, where 1 indicates that
other_word_indexwas found in the same window as
word_index, and 0 indicates that
- if categorical is set to True, the labels are categorical, ie. 1 becomes [0,1], and 0 becomes [1, 0].
- 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
sampling_table[i]is the probability of sampling the word with index i (assumed to be i-th most common word in the dataset).
Used for generating the
sampling_table argument for
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: 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).