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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
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# Unless required by applicable law or agreed to in writing, software
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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(MAE).
Creates a criterion that measures the 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}
where :math:`n` is batch size.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> 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 MAE where the shape of
`y_pred` and `y` are both N-D and the shape should be the same.
Raises:
ValueError: If the number of the input is not 2.
"""
if len(inputs) != 2:
raise ValueError("For 'MAE.update', it needs 2 inputs (predicted value, true value), "
"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(MAE).
Returns:
numpy.float64. The computed result.
Raises:
RuntimeError: If the total number of samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError("The 'MAE' can not be calculated, because the number of samples is 0, "
"please check whether your inputs(predicted value, true value) are empty, "
"or has called update method before calling eval method.")
return self._abs_error_sum / self._samples_num
[docs]class MSE(Metric):
r"""
Measures the mean squared error(MSE).
Creates a criterion that measures the MSE (squared L2 norm) between
each element in the prediction and the ground truth: :math:`x` and: :math:`y`.
.. math::
\text{MSE}(x,\ y) = \frac{\sum_{i=1}^n(y_i - x_i)^2}{n}
where :math:`n` is batch size.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> 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()
>>> print(result)
0.0031250009778887033
"""
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 the MSE where the shape of
`y_pred` and `y` are both N-D and the shape should be the same.
Raises:
ValueError: If the number of inputs is not 2.
"""
if len(inputs) != 2:
raise ValueError("For 'MSE.update', it needs 2 inputs (predicted value, true value), "
"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):
"""
Computes the mean squared error(MSE).
Returns:
numpy.float64. The computed result.
Raises:
RuntimeError: If the number of samples is 0.
"""
if self._samples_num == 0:
raise RuntimeError("The 'MSE' can not be calculated, because the number of samples is 0, "
"please check whether your inputs(predicted value, true value) are empty, "
"or has called update method before calling eval method.")
return self._squared_error_sum / self._samples_num