Source code for mindspore.nn.metrics.loss

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"""Loss for evaluation"""
from __future__ import absolute_import

from mindspore.nn.metrics.metric import Metric, rearrange_inputs


[文档]class Loss(Metric): r""" Calculates the average of the loss. If method 'update' is called every :math:`n` iterations, the result of evaluation will be: .. math:: loss = \frac{\sum_{k=1}^{n}loss_k}{n} Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array(0.2), mindspore.float32) >>> loss = nn.Loss() >>> loss.clear() >>> loss.update(x) >>> result = loss.eval() >>> print(result) 0.20000000298023224 """ def __init__(self): super(Loss, self).__init__() self.clear()
[文档] def clear(self): """Clears the internal evaluation result.""" self._sum_loss = 0 self._total_num = 0
[文档] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result. Args: inputs: Inputs contain only one element, the element is loss. The dimension of loss must be 0 or 1. Raises: ValueError: If the length of inputs is not 1. ValueError: If the dimension of loss is not 1 or 0. """ if len(inputs) != 1: raise ValueError("For 'Loss.update', it needs 1 input (loss), but got {}".format(len(inputs))) loss = self._convert_data(inputs[0]) if loss.ndim == 0: loss = loss.reshape(1) if loss.ndim != 1: raise ValueError("For 'Loss.update', the dimension of your input (loss) must be 1, " "but got {}.".format(loss.ndim)) loss = loss.mean(-1) self._sum_loss += loss self._total_num += 1
[文档] def eval(self): """ Calculates the average of the loss. Returns: numpy.float64. The average of the loss. Raises: RuntimeError: If the total number is 0. """ if self._total_num == 0: raise RuntimeError("The 'Loss' can not be calculated, because the number of samples is 0, please " "check whether has called update method before calling eval method.") return self._sum_loss / self._total_num