![]() ![]() Therefore, to identify the best settings for our unique use case, it is always a good idea to experiment with alternative loss functions and hyper-parameters. While cross-entropy loss is a strong and useful tool for deep learning model training, it's crucial to remember that it is only one of many possible loss functions and might not be the ideal option for all tasks or datasets. To summarize, cross-entropy loss is a popular loss function in deep learning and is very effective for classification tasks. For example, if the true label is 1, and the predicted probability is 0.9. Line 24: Finally, we print the manually computed loss. Cross-entropy loss works by penalizing incorrect predictions more than correct ones. Line 21: We compute the cross-entropy loss manually by taking the negative log of the softmax probabilities for the target class indices, averaging over all samples, and negating the result. The PyTorch implementations of CrossEntropyLoss and NLLLoss are slightly different in the expected input values. The essential part of computing the negative log-likelihood is to sum up the correct log probabilities. ![]() It coincides with the logistic loss applied to the outputs of a neural network, when the softmax is used. Line 18: We also print the computed softmax probabilities. Cross-entropy and negative log-likelihood are closely related mathematical formulations. Cross-entropy is a widely used loss function in applications. Line 15: We compute the softmax probabilities manually passing the input_data and dim=1 which means that the function will apply the softmax function along the second dimension of the input_data tensor. The labels argument is the true label for the corresponding input data. The input_data argument is the predicted output of the model, which could be the output of the final layer before applying a softmax activation function. ![]() 3.1 Preliminaries We consider the problem of k-class classication. Finally, we theoretically analyze the robustness of Taylor cross en-tropy loss. Then, we introduce our proposed Taylor cross entropy loss. Line 9: The TF.cross_entropy() function takes two arguments: input_data and labels. 3 Taylor Cross Entropy Loss for Robust Learning with Label Noise In this section, we rst briey review CCE and MAE. The tensor is of type LongTensor, which means that it contains integer values of 64-bit precision. Line 6: We create a tensor called labels using the PyTorch library. What is Cross Entropy Before we proceed to learn about cross-entropy loss, it’d be helpful to review the definition of cross entropy. Line 5: We define some sample input data and labels with the input data having 4 samples and 10 classes. Line 2: We also import torch.nn.functional with an alias TF. ![]()
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