critrion = nn.CrossEntropyLoss()

This line of code defines a criterion for measuring the loss in a neural network during training. Specifically, it uses the CrossEntropyLoss function from the PyTorch nn module.

Cross-entropy loss is commonly used for classification problems where the goal is to predict a class label for each input. It compares the predicted probability distribution with the true distribution of class labels and calculates a loss value based on the difference between the two. The CrossEntropyLoss function takes in the predicted class probabilities and the true class labels and returns a scalar value that represents the average loss over the entire batch of inputs.

During training, the goal is to minimize the value of this loss function, which will improve the accuracy of the model's predictions.