Source code for mindspore.nn.metrics.perplexity

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"""Perplexity"""
import math
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs


[docs]class Perplexity(Metric): r""" Computes perplexity. Perplexity is a measurement about how well a probability distribution or a model predicts a sample. A low perplexity indicates the model can predict the sample well. The function is shown as follows: .. math:: PP(W)=P(w_{1}w_{2}...w_{N})^{-\frac{1}{N}}=\sqrt[N]{\frac{1}{P(w_{1}w_{2}...w_{N})}} Args: ignore_label (int): Index of an invalid label to be ignored when counting. If set to `None`, it will include all entries. Default: -1. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Note: The method `update` must be called with the form `update(preds, labels)`. Examples: >>> import numpy as np >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) >>> y = Tensor(np.array([1, 0, 1])) >>> metric = nn.Perplexity(ignore_label=None) >>> metric.clear() >>> metric.update(x, y) >>> perplexity = metric.eval() >>> print(perplexity) 2.231443166940565 """ def __init__(self, ignore_label=None): super(Perplexity, self).__init__() if ignore_label is None: self.ignore_label = ignore_label else: self.ignore_label = validator.check_value_type("ignore_label", ignore_label, [int]) self.clear()
[docs] def clear(self): """Clears the internal evaluation result.""" self._sum_metric = 0.0 self._num_inst = 0
[docs] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result: math:preds and :math:labels. Args: inputs: Input `preds` and `labels`. `preds` and `labels` are Tensor, list or numpy.ndarray. `preds` is the predicted values, `labels` is the label of the data. The shape of `preds` and `labels` are both :math:`(N, C)`. Raises: ValueError: If the number of the inputs is not 2. RuntimeError: If preds and labels have different lengths. RuntimeError: If label shape is not equal to pred shape. """ if len(inputs) != 2: raise ValueError('The perplexity needs 2 inputs (preds, labels), but got {}.'.format(len(inputs))) preds = [self._convert_data(inputs[0])] labels = [self._convert_data(inputs[1])] if len(preds) != len(labels): raise RuntimeError('The preds and labels should have the same length, but the length of preds is{}, ' 'the length of labels is {}.'.format(len(preds), len(labels))) loss = 0. num = 0 for label, pred in zip(labels, preds): if label.size != pred.size / pred.shape[-1]: raise RuntimeError("shape mismatch: label shape should be equal to pred shape, but got label shape " "is {}, pred shape is {}.".format(label.shape, pred.shape)) label = label.reshape((label.size,)) label_expand = label.astype(int) label_expand = np.expand_dims(label_expand, axis=1) first_indices = np.arange(label_expand.shape[0])[:, None] pred = np.squeeze(pred[first_indices, label_expand]) if self.ignore_label is not None: ignore = (label == self.ignore_label).astype(pred.dtype) num -= np.sum(ignore) pred = pred * (1 - ignore) + ignore loss -= np.sum(np.log(np.maximum(1e-10, pred))) num += pred.size self._sum_metric += loss self._num_inst += num
[docs] def eval(self): r""" Returns the current evaluation result. Returns: float, the computed result. Raises: RuntimeError: If the sample size is 0. """ if self._num_inst == 0: raise RuntimeError('The perplexity can not be calculated, because the number of samples is 0.') return math.exp(self._sum_metric / self._num_inst)