Source code for mindspore.train.metrics.error

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Error."""
from __future__ import absolute_import

import numpy as np

from mindspore.train.metrics.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\_pred}_i - y_i\|}{n} where :math:`n` is batch size. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore >>> from mindspore import Tensor >>> from mindspore.train import MAE >>> >>> 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 = 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\_pred}_i - y_i)^2}{n} where :math:`n` is batch size. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore >>> from mindspore import Tensor >>> from mindspore.train import MSE >>> >>> 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 = 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