Binary crossentropy is a loss function that tells you how good your model’s predictions are.

It is used to make yes or no decisions, and can optimize several such decisions at once. A typical case is the multi-label classification problem,.  where an example can belong (or not) to multiple categories at the same time

Take for instance the mood of a piece of music. Every piece can have more than one mood, for instance, it can be both "Happy" and "Energetic", at the same time. 

You can actually learn how to do this in our tutorial Predicting mood from raw audio data.

On the Peltarion Platform you set the loss function in the Target block. 

Remember that the block before the Target block must use Sigmoid as activation function.

So the math. This is the formula for Binary crossentropy where ŷ is the predicted value.
Binary crossentropy measures how far away from the target value (which is either 0 or 1) the prediction is for each of the classes and then sums up these class-wise errors to obtain the final loss.

You can read more about how you can use binary crossentropy on the Peltarion Knowledge center. But you always learn best if you try it out. So sign-up and try it out yourself.

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