__nouse, __loss_t = __session_t.run([__train_op, __loss_cross_entropy], feed_dict={__X_input: __x_batch, __Y_true: __y_batch}) __avg_lost += float(__loss_t)/__total_batch if epoch % __display_step == 0: print("Epoch:", '%04d' % (epoch+1), "Avg_Loss=",...
importtorchdefsafe_mse_loss(y_true,y_pred):# 强制将无穷大和NaN替换为0y_true=torch.nan_to_num(y_true,nan=0.0,posinf=0.0,neginf=0.0)y_pred=torch.nan_to_num(y_pred,nan=0.0,posinf=0.0,neginf=0.0)returntorch.mean((y_true-y_pred)**2) 1. 2. 3. 4. 5. 6. 7. AI检测代码解析...
在Python中,实现均方误差损失函数(MSELoss)通常涉及到计算预测值与真实值之差的平方,并求其平均值。以下是实现这一功能的步骤: 理解均方误差损失函数的数学原理: 均方误差(MSE)是衡量模型预测值与实际值之间差异的一种常用方法。其计算公式为: [ \text{MSE} = \frac{1}{N} \sum_{i=1}^{N} (y_i -...
1、均方误差,二次损失,L2损失(MeanSquareError, Quadratic Loss, L2 Loss)均方误差(MSE)是最常用的回归损失函数。MSE是目标变量与预测值之间距离平方之和。下面是一个MSE函数的图,其中真实目标值为100,预测值在-10,000至10,000之间。预测值(X轴)= 100时,MSE损失(Y轴)达到其最小值。损失范围为0至∞。MSE...
pythonCopy codeimporttorchimporttorch.nnasnn # 创建预测值和目标值 y_pred=torch.tensor([0.5,1.0,2.0])y_actual=torch.tensor([1.0,2.0,3.0])# 创建MSELoss函数 mse_loss=nn.MSELoss()# 计算MSELoss值 loss=mse_loss(y_pred,y_actual)print(loss) ...
```python import tensorflow as tf mse_loss = tf.keras.losses.MeanSquaredError() mse_value = mse_loss(y_true, y_pred) print(mse_value) #输出:1.6666666666666667 ``` 在这段代码中,我们首先导入TensorFlow库,然后创建一个`MeanSquaredError`对象作为MSE损失函数。最后,我们调用该对象的`call`方法来计算...
MSE是mean squared error的缩写,即平均平方误差,简称均方误差。 MSE是逐元素计算的,计算公式为: 旧版的nn.MSELoss()函数有reduce、size_average两个参数,新版的只有一个reduction参数了,功能是一样的。reduction的意思是维度要不要缩减,
下面的是python的例子: 1#-*- coding: utf-8 -*-23importtorch4importtorch.optim as optim56loss_fn = torch.nn.MSELoss(reduce=False, size_average=False)7#loss_fn = torch.nn.MSELoss(reduce=True, size_average=True)8#loss_fn = torch.nn.MSELoss()9input = torch.autograd.Variable(torch.ran...
loss_1=loss_fn_1(inputs.float(),targets.float())print(loss_1)#***#2、返回平均值 #***loss_fn_2=torch.nn.MSELoss(reduction='mean')#将Variable类型统一为float()(tensor类型也是调用xxx.float()) loss_2=loss_fn_2(inputs.float(),targets.float())print(loss_2)#***...
from sklearn.metrics import log_loss, roc_auc_score from collections import OrderedDict, namedtuple, defaultdict from BaseModel.basemodel import BaseModel class DeepFM(BaseModel): def __init__(self, config, feat_sizes, sparse_feature_columns, dense_feature_columns): ...