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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import paddle
import numpy as np
import paddle.nn.functional as F
[docs]class Perplexity(paddle.metric.Metric):
"""
Perplexity is calculated using cross entropy. It supports both padding data
and no padding data.
If data is not padded, users should provide `seq_len` for `Metric`
initialization. If data is padded, your label should contain `seq_mask`,
which indicates the actual length of samples.
This Perplexity requires that the output of your network is prediction,
label and sequence length (opitonal). If the Perplexity here doesn't meet
your needs, you could override the `compute` or `update` method for
caculating Perplexity.
Args:
seq_len(int): Sequence length of each sample, it must be provided while
data is not padded. Default: 20.
name(str): Name of `Metric` instance. Default: 'Perplexity'.
"""
def __init__(self, name='Perplexity', *args, **kwargs):
super(Perplexity, self).__init__(*args, **kwargs)
self._name = name
self.total_ce = 0
self.total_word_num = 0
[docs] def compute(self, pred, label, seq_mask=None):
label = paddle.unsqueeze(label, axis=2)
ce = F.softmax_with_cross_entropy(
logits=pred, label=label, soft_label=False)
ce = paddle.squeeze(ce, axis=[2])
if seq_mask is not None:
ce = ce * seq_mask
word_num = paddle.sum(seq_mask)
return ce, word_num
return ce
[docs] def update(self, ce, word_num=None):
batch_ce = np.sum(ce)
if word_num is None:
word_num = ce.shape[0] * ce.shape[1]
else:
word_num = word_num[0]
self.total_ce += batch_ce
self.total_word_num += word_num
[docs] def reset(self):
self.total_ce = 0
self.total_word_num = 0
[docs] def accumulate(self):
return np.exp(self.total_ce / self.total_word_num)
[docs] def name(self):
return self._name