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Why is validation loss much higher than training loss?
In general, if you’re seeing much higher validation loss than training loss, then it’s a sign that your model is overfitting – it learns “superstitions” i.e. patterns that accidentally happened to be true in your training data but don’t have a basis in reality, and thus aren’t true in your validation data.
When validation accuracy is higher than training?
The training loss is higher because you’ve made it artificially harder for the network to give the right answers. However, during validation all of the units are available, so the network has its full computational power – and thus it might perform better than in training.
What to do about validation loss in machine learning?
Really a fundamental question in machine learning. If validation loss >> training loss you can call it overfitting. If validation loss > training loss you can call it some overfitting. If validation loss < training loss you can call it some underfitting. If validation loss << training loss you can call it underfitting.
Which is better training data or validation data?
As I understand, a model usually fits better training data than validation data but I am getting the opposite results. Loss is lower and accuracy is higher for validation set. As I have read in other answers, this can be because of dropout not being applied during validation.
Is it a good thing to overfit in deep learning?
Some overfitting is nearly always a good thing. All that matters in the end is: is the validation loss as low as you can get it. This often occurs when the training loss is quite a bit lower. Also check how to prevent overfitting.
What is the validation loss for epoch 20 / 20-14?
Epoch 20/20 – 14s – loss: 0.0510 – val_loss: 188.9537 I assume I must be doing something obvious wrong, but can’t realize it since I’m a newbie. I am hoping to either get some useful validation loss achieved (compared to training), or know that my data observations are simply not large enough for useful LSTM modeling.