However, in principle the cross entropy loss can be calculated.

Many implementations will require your ground truth values to be one-hot encoded (with a single true class), because that allows for some extra optimization.

For a single example, it would look like this: However many activations will not be compatible with the calculation because their outputs are not interpretable as probabilities.Īs we discussed in fast_ai, Softmax is often used for multiclass classification because it guarantees a well-behaved probability distribution function.įor a neural network, you will usually see the equation written in a form where yy is the ground truth vector and y^y^ (or some other value taken direct from the last layer output) is the estimate. Cross-entropy loss increases as the predicted probability diverges from the actual label.įor a neural network, the calculation is independent of the following parameters: The cross entropy formula takes in two distributions, p(x) p(x), the true distribution, and q(x)q(x), the estimated distribution, defined over the discrete variable xx and is given byĬross-entropy loss, measures the performance of a classification model whose output is a probability value between 0 and 1. In this case, we would calculate our prediction for each image, collect these values to calculate an overall accuracy, and then calculate the gradients of each weight with respect to that overall accuracy. The approach would be to use accuracy, which is our metric, as our loss function as well. Therefore, we need to choose a loss function.

That is because the gradients are a measure of how that loss function changes with small tweaks to the weights. In Fast_AI, we discuss about Cross entropy Loss, it is basically the function that maps an event or values of one or more variables onto a real number in this way we used gradients have been useful to improve our model using SGD, and in order to calculate gradients we need some loss function that represents how good our model is. UNPACK_AI : Loss Entropy Loss explained in Fast_AI #

Entropy explained