What to do if your training loss is lower than Your Validation loss?
If your training loss is much lower than validation loss then this means the network might be overfitting. Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting.
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.
How long does it take for validation loss to go down?
It also takes only about 15 – 20 epochs to get the Validation Loss to around 0.5 before it won’t go any lower, or starts going back up. Each “card” is between 100 – 400 characters long. The cards have been pre-shuffled (mainly so like colored cards are not next to eachother).
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.
How do I reduce my validation loss graph?
Following is the model architecture : Attached is the graph of the neural network output. The ‘validation loss’ metrics from the test data has been oscillating a lot after epochs but not really decreasing. Can anyone explain how to interpret the graph ?
How to reduce validation loss in CNN model?
As is already mentioned, it is pretty hard to give a good advice without seeing the data. What I would try is the following: – remove the Dropout after the maxpooling layer – remove some dense layer – add dropout between dense the highest priority is, to get more data.