A commonly used loss function in image generation tasks using Pytorch is the Binary Cross Entropy Loss (BCELoss). This function measures the error between the network's output and target values. In image generation tasks, the target is the original image and the output is the generated image. BCELoss is suitable when the activation function is a sigmoid. The Mean Squared Error (MSE) loss can be another good choice. It calculates the squared difference between the network's output and target values. This is particularly useful when the activation function used is a linear one. Lastly, if you are using GANs for image generation, you might incorporate the Generative Adversarial Loss. This special type of loss function features competition between two neural networks – generator (which generates fake samples) and discriminator (which distinguishes the fake from the real). Each loss function has its pros and cons, so it's essential to understand the requirements of your model and adjust the choice accordingly. Here's how you can implement them in PyTorch: - For BCELoss: ```python loss_function = nn.BCELoss() ``` - For MSELoss: ```python loss_function = nn.MSELoss() ``` Remember that these loss functions require your predictions and the target to be of the same data type (usually float) and the same shape. If they are not, you will need to cast them explicitly.