tf.keras.preprocessing.sequence.make_sampling_table( size, sampling_factor=1e-05 )
Defined in tensorflow/python/keras/_impl/keras/preprocessing/sequence.py
.
Generates a word rank-based probabilistic sampling table.
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).
The sampling probabilities are generated according to the sampling distribution used in word2vec:
p(word) = min(1, sqrt(word_frequency / sampling_factor) / (word_frequency / sampling_factor))
We assume that the word frequencies follow Zipf's law (s=1) to derive a numerical approximation of frequency(rank):
frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))
where gamma
is the Euler-Mascheroni constant.
size
: Int, number of possible words to sample.sampling_factor
: The sampling factor in the word2vec formula.A 1D Numpy array of length size
where the ith entry is the probability that a word of rank i should be sampled.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/make_sampling_table