Is Jensen-Shannon symmetric?

The Jensen-Shannon distance is symmetric, meaning JS(P,Q) = JS(Q,P). This is in contrast to Kullback-Leibler divergence which is not symmetric, meaning KL(P,Q) != KL(Q,P) in general. Jensen-Shannon is not used very often.

Is JS divergence a distance?

It is more useful as a measure as it provides a smoothed and normalized version of KL divergence, with scores between 0 (identical) and 1 (maximally different), when using the base-2 logarithm. The square root of the score gives a quantity referred to as the Jensen-Shannon distance, or JS distance for short.

Why is JS divergence better than KL divergence?

KL is very widely used in statistics, signal processing and machine learning, JS less so. One significant advantage of JS is that it is a metric — symmetry and triangle inequality.

How is KL calculated?

KL divergence can be calculated as the negative sum of probability of each event in P multiplied by the log of the probability of the event in Q over the probability of the event in P. The value within the sum is the divergence for a given event.

What is binary Crossentropy?

Binary crossentropy is a loss function that is used in binary classification tasks. These are tasks that answer a question with only two choices (yes or no, A or B, 0 or 1, left or right).

What is entropy in NLP?

The entropy is the expected value of the surprisal across all possible events indexed by i: Entropy of a probability distribution p. So, the entropy is the average amount of surprise when something happens.

What is KL divergence used for?

The Kullback-Leibler Divergence score, or KL divergence score, quantifies how much one probability distribution differs from another probability distribution. The KL divergence between two distributions Q and P is often stated using the following notation: KL(P || Q)

When should I use KL divergence?

As we’ve seen, we can use KL divergence to minimize how much information loss we have when approximating a distribution. Combining KL divergence with neural networks allows us to learn very complex approximating distribution for our data.

What is a good value for KL divergence?

Intuitively this measures the how much a given arbitrary distribution is away from the true distribution. If two distributions perfectly match, D_{KL} (p||q) = 0 otherwise it can take values between 0 and ∞. Lower the KL divergence value, the better we have matched the true distribution with our approximation.

What is Logloss?

Log Loss is the negative average of the log of corrected predicted probabilities for each instance. Let us understand it with an example: The model is giving predicted probabilities as shown above.

What is BCELoss?

BCELoss creates a criterion that measures the Binary Cross Entropy between the target and the output. You can read more about BCELoss here. If we use BCELoss function we need to have a sigmoid layer in our network.