# 程序代写代做代考 python Keras ## pad_sequences

“`python
“`

Transform a list of `num_samples` sequences (lists of scalars) 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 end. Sequences longer than `num_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 `(num_samples, num_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

“`python
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](http://arxiv.org/pdf/1301.3781v3.pdf)

– __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

“`python
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).