11/20/2023 0 Comments Pytorch cross entropy loss![]() ![]() Therefore loss functions are essential for training a deep learning model. Loss functions are responsible for evaluating the cost (the difference between the model’s output and the ground truth) and pointing the model in the right direction, so it corrects its weights for accurate output. This ensures that the input data is mapped to the correct output values or labels. Understand that the goal of training a neural network is to optimize the weights of its neurons. This means that deep learning engineers need not define everything from scratch they can define the functions and let PyTorch do all of the background calculations. Furthermore, PyTorch has automatic differentiation built-in as well, freeing us from the hassle of calculating gradients. The great thing about PyTorch is that not only will it take care of the graph for us, but also let us create custom activation and loss functions that will automatically add to the graph. These variables will be used for calculating gradients in the backpropagation step of training the model. ![]() Additionally, a computational graph is necessary for keeping track of the variables involved in the calculations that give us output from the model. What are the computational graphs you might ask? In layman’s terms, a computational graph is a sequence of operations a deep learning model will perform. PyTorch has been, for the longest time, the preferred deep learning framework due to its ability to create robust dynamic computational graphs. In this article, we focus mainly on PyTorch and how we can define custom loss functions using its NN and AUTOGRAD modules. However, it was not until the inception of deep learning toolsets like PyTorch and Tensorflow that deep learning became accessible to the everyday joe.
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