其中,MLSE(Mean Logarithmic Squared Error)损失函数是一种常用的回归问题损失函数。本文将介绍MLSE损失函数的原理和在PyTorch中的实现。 回归问题是指预测一个连续值的问题,如房价预测、股票价格预测等。在回归问题中,我们需要评估模型的预测值与真实值之间的差距。而损失函数就是用来衡量这种差距的指标。MLSE损失函数是...
MSE | mean_squared_error tf.keras.losses.MSE(y_true, y_pred) 公式:loss = mean(square(y_true - y_pred), axis=-1) MeanSquaredLogarithmicError tf.keras.losses.MeanSquaredLogarithmicError(reduction=losses_utils.ReductionV2.AUTO, name='mean_squared_logarithmic_error') 公式:loss = square(log(...
1. RMSLE(Root Mean Square Logarithmic Error) 2. RMSPE(Root Mean Square Percentage Error) 对于数值序列出现长尾分布的情况,可以选择MSLE(Mean squared logarithmic error,均方对数误差),对原有数据取对数后再进行比较(公式中+1是为了避免数值为0时出现无穷值)。 0 3 R²系列 R²(R squared, Coefficient ...
tf.keras.losses.MeanSquaredLogarithmicError(reduction=losses_utils.ReductionV2.AUTO, name='mean_squared_logarithmic_error')公式:loss = square(log(y_true + 1.) - log(y_pred + 1.))参数: reduction:传入tf.keras.losses.Reduction类型值,默认AUTO,定义对损失的计算方式。 1. MSLE | mean_squared_l...
squeeze(f @ w, 1) return yhat def compute_loss(y, yhat): # The loss is defined to be the mean squared error distance between our # estimate of y and its true value. loss = torch.nn.functional.mse_loss(yhat, y) return loss def generate_data(): # Generate some training data ...
reduction:string类型,'none'|'mean'|'sum'三种参数值 BCEWithLogitsLoss[8]:其实和TensorFlow是的`from_logits`参数很像,在BCELoss的基础上合并了Sigmoid 代码语言:javascript 复制 torch.nn.BCEWithLogitsLoss(weight:Optional[torch.Tensor]=None,size_average=None,reduce=None,reduction:str='mean',pos_weight:...
Mean Squared Error[fb:None]: 275233.8125 Mean Squared Error[fb:slaney]: 3064.0825 Mean Squared Error[fb:librosa_auditory_toolbox+slaney]: 0.0000 Result says that once the exactly same kernel and normalization are used, the result comply the librosa output!
在讲解具体的损失函数之前,我们有必要了解下什么是损失函数。所谓损失函数,指的是衡量模型预测值y与真实标签Y之间的差距的函数。本文将介绍的损失函数如下:Mean Squared Error(均方差损失函数)Mean Absolute Error(绝对值损失函数)Binary iou损失 pytorch ...
均方误差(Mean Squared Error,MSE):均方误差是最常见的图像恢复损失函数之一。它计算恢复图像与原始图像之间的像素级别差异的平均值的平方。MSE损失函数趋向于使恢复图像的像素值与原始图像的像素值尽可能接近。均方根误差(Root Mean Squared Error,RMSE):均方根误差是均方误差的平方根,它衡量恢复图像与原始图像之间的...
Regression: Mean Squared Error or mean squared error, mse for short. Binary Classification (2 class): Logarithmic Loss, also called cross entropy or binary crossentropy. Multiclass Classification (> 2 class): Multiclass Logarithmic Loss or categorical crossentropy. 优化方法 Stochastic Gradient Descent...