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What is validation loss and accuracy?
It is the sum of errors made for each example in training or validation sets. Loss value implies how poorly or well a model behaves after each iteration of optimization. An accuracy metric is used to measure the algorithm’s performance in an interpretable way.
What is difference between validation accuracy and accuracy?
In other words, the test (or testing) accuracy often refers to the validation accuracy, that is, the accuracy you calculate on the data set you do not use for training, but you use (during the training process) for validating (or “testing”) the generalisation ability of your model or for “early stopping”.
How is it possible that validation loss should increase?
After some time, validation loss started to increase, whereas validation accuracy is also increasing. The test loss and test accuracy continue to improve. How is this possible? It seems that if validation loss increase, accuracy should decrease.
When does validation loss and accuracy decrease in Python?
Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s.
When do you stop training for validation loss?
At the end of 1st epoch validation loss started to increase, whereas validation accuracy is also increasing. Can i call this over fitting? I’m thinking of stopping the training after 6th epoch. My criteria would be: stop if the accuracy is decreasing. Is there something really wrong going on?
When does ACC increase and validation loss decrease?
When I start training, the acc for training will slowly start to increase and loss will decrease where as the validation will do the exact opposite. I have really tried to deal with overfitting, and I simply cannot still believe that this is what is coursing this issue.