# 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...
2、还有其它属于Sigmoid functions的函数: 3、我们知道线性回归(Linear Regression)的损失函数为:loss = (y_pred - y) ** 2 = (x * w - y) ** 2 ,这更是求两个值的距离差。相对于线性回归损失函数,二值化分类(Binary Classification)损失函数(BCELoss):loss = - (y * log(y_pred) + (1 - y...
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...
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nn.MSELoss, 定义：(y- y_pred) ^ 2 , 惩罚big mistakes, default loss function for regression...
model =LinearRegressionModel() # 定义损失函数和优化器 criterion = nn.MSELoss() optimizer = optim.SGD(model.parameters(), lr=0.1) # 训练模型 epochs =100forepochinrange(epochs): model.train() optimizer.zero_grad() outputs =model(x_tensor) ...
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 ...
Logistic Regression 逻辑回归 - 二分类 原来的: graph LR x-->LinearLinear-->y \hat{y} = x * w + b loss = \frac{1}{N}\sum_{n=1}^{N}(\hat{y_n}-y_n)^2 激活函数: using sigmoid functions: graph LR x--> LinearLinear--> SigmoidSigmoid--> y ...
'_unique', '_unique2', '_unpack_dual', '_use_cudnn_ctc_loss', '_use_cudnn_rnn_flatten_weight', '_validate_sparse_coo_tensor_args', '_validate_sparse_csr_tensor_args', '_weight_norm', '_weight_norm_cuda_interface', 'abs', 'abs_', 'absolute', 'acos', 'acos_', 'acosh',...
Pytorch lightning 2.0.5 used for the experiment What version are you seeing the problem on? v2.0 How to reproduce the bug deftest_compiled_model_to_log_metric_with_cpu(tmp_path):classMyModel(BoringModel):deftraining_step(self,batch,batch_idx):loss=self.step(batch)self.log("loss",loss)re...