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. target ( Tensor) Ground truth class indices or class probabilities see Shape section below for supported shapes. Parameters: input ( Tensor) Predicted unnormalized logits see Shape section below for supported shapes. To summarize, cross-entropy loss is a popular loss function in deep learning and is very effective for classification tasks. This criterion computes the cross entropy loss between input logits and target. Line 24: Finally, we print the manually computed loss. 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. Next, we specify the loss function as cross-entropy loss and the optimizer as Adam: The Adam optimizer is a robust, gradient-based optimization method. Line 18: We also print the computed softmax probabilities. For example, consider a scenario where the cost of misclassifying certain classes is much higher than others. However, in some cases, the cross-entropy loss may not be the best choice for a particular task.
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 cross-entropy loss calculates the difference between the predicted probability distribution and the actual probability distribution. The labels argument is the true label for the corresponding input data. Let’s understand the graph below which shows what influences hyperparameters \alpha and \gamma has on. Important point to note is when \gamma 0 0, Focal Loss becomes Cross-Entropy Loss. 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. The only difference between original Cross-Entropy Loss and Focal Loss are these hyperparameters: alpha ( \alpha ) and gamma ( \gamma ). Line 9: The TF.cross_entropy() function takes two arguments: input_data and labels. 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. Line 5: We define some sample input data and labels with the input data having 4 samples and 10 classes. BCELoss class torch.nn.BCELoss(weightNone, sizeaverageNone, reduceNone, reduction'mean') source Creates a criterion that measures the Binary Cross Entropy between the target and the input probabilities: The unreduced (i.e. Usually, when using Cross Entropy Loss, the output of our. Line 2: We also import torch.nn.functional with an alias TF. It measures the difference between two probability distributions for a given set of random variables.
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