What if test error is less than train error?

If your test error is less than the training error, this means that there is a sampling bias in your test.

Why is test loss lower than training loss?

Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.

What does it mean if validation loss is less than training 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 if train accuracy is less than test accuracy?

Typically you should have test accuracy less than of the train accuracy. Test data is data unseen by your model, and train data is the data your model use to train itself. So I would say it is more likely luck that you have test accuracy higher than train accuracy.

What happens when testing error is more than training error?

Test error is consistently higher than training error: if this is by a small margin, and both error curves are decreasing with epochs, it should be fine. However if your test set error is not decreasing, while your training error is decreasing alot, it means you are over fitting severely.

Why is test accuracy lower than train accuracy?

Test accuracy should not be higher than train since the model is optimized for the latter. Ways in which this behavior might happen: you did not use the same source dataset for test. You should do a proper train/test split in which both of them have the same underlying distribution.

Should validation loss be lower than training?

Generally speaking though, training error will almost always underestimate your validation error. However it is possible for the validation error to be less than the training.

Why test accuracy is low?

A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specalized to the structure in the training dataset.

How much does it cost to take the CNA exam?

The exam fees will vary from state to state, but as an example of cost, in California the NNAAP exam is $90 for the written version of the exam and $105 for the audio version. Find out more about CNA Certification in your state.

What is the cost of attendance (COA) at CNM?

Learn about the cost of attendance at CNM. Cost of Attendance (COA) or Student Budget is your educational expenses for a given period of enrollment, e.g., fall, spring, and summer.

What are the advantages of using more layers in CNN?

Furthermore, The more filters deployed, the more features that CNN will extract. This allows more features found but with the cost of more training time. There is a sweet spot for the number of layers, usually, I will put 6 for 150 x 150 size of image.

How much does it cost to get NNAAP certified?

In some cases, the certification exam fee may be included in your training program cost. The exam fees will vary from state to state, but as an example of cost, in California the NNAAP exam is $90 for the written version of the exam and $105 for the audio version.