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What is BCE loss function?
BCE loss is used for the binary classification tasks. If you are using BCE loss function, you just need one output node to classify the data into two classes. The output value should be passed through a sigmoid activation function and the range of output is (0 – 1).
What is the range of cross-entropy loss?
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. So predicting a probability of .
How is CE loss defined in binary classification problem?
As usually an activation function (Sigmoid / Softmax) is applied to the scores before the CE Loss computation, we write f (si) f ( s i) to refer to the activations. In a binary classification problem, where C′ = 2 C ′ = 2, the Cross Entropy Loss can be defined also as [discussion]:
When to use one neuron instead of two?
This is perfectly valid for two classes, however, one can also use one neuron (instead of two) given that its output satisfies: 0 ≤ y ≤ 1 for all inputs. This can be assured if a transformation (differentiable/smooth for backpropagation purposes) is applied which maps a to y such that the above condition is met.
How is cross entropy loss defined in binary classification?
In a binary classification problem, where C ′ = 2, the Cross Entropy Loss can be defined also as [discussion]: Where it’s assumed that there are two classes: C1 and C2. t1 [0,1] and s1 are the groundtruth and the score for C1, and t2 = 1 − t1 and s2 = 1 − s1 are the groundtruth and the score for C2.
Are there loss terms coming from negative classes?
The loss terms coming from the negative classes are zero. However, the loss gradient respect those negative classes is not cancelled, since the Softmax of the positive class also depends on the negative classes scores.