What’s the meaning of loss & acc ? I mean they are loss and accuracy of training data but why the sum is not equal to 1?
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Accuracy is a method for measuring performance based on the actual value and the predicted value.
Loss is a performance measure that is based on how much the predicted value varies from the actual value.
As they are different performance measures, their sum is not 1 (in most cases).
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acc refers to how often your model predicted the correct class.
loss refers to how close to the label your predictions were.
Consider a single binary classification where your prediction is 0.9 and your label is 1 (I’ll use mean absolute error as my loss function). Your prediction was closer to 1 than it was to 0, so it predicted correctly 1 out of 1 times, and your acc is 1.0 (100%). Your loss is |0.9 - 1| = 0.1, so you were close, but your model will use that loss to make its next prediction closer.
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Got it, they are the respective values instead of being some probabilities or percentages obtained by simple subtraction.
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