# Define the loss function - For classification problem loss_function = nn.CrossEntropyLoss() # Define the loss function - For regression problem loss_function = nn.MSELoss() # Mean Squared Error loss 另请注意,关于损失函数的选择和处理,可以应用一些额外的考虑因素和技术。 其中一些例子是: 自定义...
如果size_average = True,返回 loss.sum(); 为了更好地理解损失函数的定义以下代码部分将这两个参数均设置为False 一般来说,工程实践中常用的损失函数大致可以分成两大应用情况:回归(Regression)和分类(Classification) 二.回归模型 1.nn.MSE...
I'm training a CNN architecture to solve a regression problem using PyTorch where my output is a tensor of 20 values. I planned to use RMSE as my loss function for the model and tried to use PyTorch's nn.MSELoss() and took the square root for it using torch.sqrt() for that but g...
Loss combinations: In some cases, combining multiple loss functions can improve model performance. For instance, using a combination of cross entropy loss and MSE loss for regression tasks may be beneficial. Backpropagation: When using BCE loss, be careful about the sign of the gradient during ba...
1 回归损失(Regression Loss) 1.1 均方误差(Mean Square Error,MSE)/ 二次损失(Quadratic loss) / L2损失(L2 Loss) 均方误差(MSE)是最常用的回归损失函数。MSE是目标变值和预测值之间距离的平方之和。 下图是MSE的函数图,真实目标值为100,预测值在-10000到10000之间。MSE损失(y轴)在预测(x轴)为100时达到最...
torch.nn - PyTorch 1.9.0 documentation.https://pytorch.org/docs/stable/nn.html#loss-functions ...
(1, 1) lr = 0.001 for ii in range(2000): x, y = get_fake_data() y_pred = x.mm(w) + b.expand_as(y) loss = 0.5 * (y_pred - y) ** 2 loss = loss.sum() dloss = 1 dy_pred = dloss * (y_pred - y) dw = x.t().mm(dy_pred) db = dy_pred.sum() w.sub_...
Get an introduction to PyTorch, then learn how to use it for a simple problem like linear regression — and a simple way to containerize your application.
For a regression problem, mean squared error is the most common loss function. The stochastic gradient descent (SGD) algorithm is the most rudimentary technique and in many situations the Adam algorithm gives better results. The demo program uses a simple approach for batching training items. For...
回归(regression)是能为一个或多个自变量与因变量之间关系建模的一类方法。 在自然科学和社会科学领域,回归经常用来表示输入和输出之间的关系。 在机器学习领域中的大多数任务通常都与预测(prediction)有关。 当我们想预测一个数值时,就会涉及到回归问题。 常见的例子包括:预测价格(房屋、股票等)、预测住院时间(针对...