torch.nn.BCELoss

BCELoss stands for Binary Cross Entropy Loss. It is a loss function used for binary classification problems where each example belongs to one of two classes. The BCELoss function computes the binary cross-entropy loss between the input and target.

The input to BCELoss is a tensor of predicted probabilities (values between 0 and 1) for each example, and the target is a tensor of binary labels (0 or 1) indicating the true class for each example. The BCELoss function applies the binary cross-entropy formula to compute the loss for each example and then averages the losses over all examples.

The formula for binary cross-entropy loss is:

loss(x, y) = -[y * log(x) + (1 - y) * log(1 - x)]

where x is the predicted probability and y is the binary label.

BCELoss is often used in binary classification problems such as spam detection, fraud detection, and medical diagnosis. It is implemented in PyTorch as torch.nn.BCELoss.