Python Natural Language Processing

Loss functions

Sometimes, loss functions are also referred to as cost functions or error functions. A loss function gives us an idea of how good the ANN performs with respect to the given training examples. So first, we define the error function and when we start to train our ANN, we will get the output. We compare the generated output with the expected output given as part of the training data and calculate the gradient value of this error function. We backpropagate the error gradient in the network so that we can update the existing weights and bias values to optimize our generated output. The error function is the main part of the training. There are various error functions available. If you ask me which error function to choose, then ...