Source code for mindspore.nn.metrics.error

# Copyright 2020 Huawei Technologies Co., Ltd
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"""Error."""
import numpy as np
from .metric import Metric, rearrange_inputs


[docs]class MAE(Metric): r""" Calculates the mean absolute error. Creates a criterion that measures the mean absolute error (MAE) between each element in the input: :math:`x` and the target: :math:`y`. .. math:: \text{MAE} = \frac{\sum_{i=1}^n \|y_i - x_i\|}{n} Here :math:`y_i` is the prediction and :math:`x_i` is the true value. Note: The method `update` must be called with the form `update(y_pred, y)`. Examples: >>> import numpy as np >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32) >>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32) >>> error = nn.MAE() >>> error.clear() >>> error.update(x, y) >>> result = error.eval() >>> print(result) 0.037499990314245224 """ def __init__(self): super(MAE, self).__init__() self.clear()
[docs] def clear(self): """Clears the internal evaluation result.""" self._abs_error_sum = 0 self._samples_num = 0
[docs] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result :math:`y_{pred}` and :math:`y`. Args: inputs: Input `y_pred` and `y` for calculating mean absolute error where the shape of `y_pred` and `y` are both N-D and the shape are the same. Raises: ValueError: If the number of the input is not 2. """ if len(inputs) != 2: raise ValueError('Mean absolute error need 2 inputs (y_pred, y), but got {}'.format(len(inputs))) y_pred = self._convert_data(inputs[0]) y = self._convert_data(inputs[1]) abs_error_sum = np.abs(y.reshape(y_pred.shape) - y_pred) self._abs_error_sum += abs_error_sum.sum() self._samples_num += y.shape[0]
[docs] def eval(self): """ Computes the mean absolute error. Returns: Float, the computed result. Raises: RuntimeError: If the number of the total samples is 0. """ if self._samples_num == 0: raise RuntimeError('Total samples num must not be 0.') return self._abs_error_sum / self._samples_num
[docs]class MSE(Metric): r""" Measures the mean squared error. Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input: :math:`x` and the target: :math:`y`. .. math:: \text{MSE}(x,\ y) = \frac{\sum_{i=1}^n(y_i - x_i)^2}{n} where :math:`n` is batch size. Examples: >>> import numpy as np >>> from mindspore import nn, Tensor >>> >>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32) >>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32) >>> error = nn.MSE() >>> error.clear() >>> error.update(x, y) >>> result = error.eval() """ def __init__(self): super(MSE, self).__init__() self.clear()
[docs] def clear(self): """Clear the internal evaluation result.""" self._squared_error_sum = 0 self._samples_num = 0
[docs] @rearrange_inputs def update(self, *inputs): """ Updates the internal evaluation result :math:`y_{pred}` and :math:`y`. Args: inputs: Input `y_pred` and `y` for calculating mean square error where the shape of `y_pred` and `y` are both N-D and the shape are the same. Raises: ValueError: If the number of input is not 2. """ if len(inputs) != 2: raise ValueError('Mean squared error need 2 inputs (y_pred, y), but got {}'.format(len(inputs))) y_pred = self._convert_data(inputs[0]) y = self._convert_data(inputs[1]) squared_error_sum = np.power(y.reshape(y_pred.shape) - y_pred, 2) self._squared_error_sum += squared_error_sum.sum() self._samples_num += y.shape[0]
[docs] def eval(self): """ Compute the mean squared error. Returns: Float, the computed result. Raises: RuntimeError: If the number of samples is 0. """ if self._samples_num == 0: raise RuntimeError('The number of input samples must not be 0.') return self._squared_error_sum / self._samples_num