# Lesson1: train_loss vs. valid_loss vs. error_rate

Hello Community,

i just finished lesson 1 and imported my own dataset.
I got a couple of questions to better interpret my results:

1. Are train_loss and valid_loss percentages? If so what does it mean if they are above 1?
2. What does it mean if train_loss and valid_loss stay high (above 1 or 2) but the error rate or accuracy gets pretty good (below 0.05)?
3. What does it mean when the train_loss is reducing nicely near to 0 but the valid_loss is stuck on above 1?
4. In python notebook if I do e.g. `learn.fit_one_cycle(8)` and follow up by an other `learn.fit_one_cycle(5)` is that the same as doing an initial `learn.fit_one_cycle(13)` ?

Thank you for the help

No, they are the values on your loss function (found in `learn.loss_func`), which is normally CrossEntropy.

It’s moreso if the train and validation loss are dropping while your metric (here it is `accuracy`) is either increasing or decreasing (whatever is good)

You’re overfitting

No, it is not. `fit_one_cycle` has some special paramters that it calls during `fit`, specifically it warms up the optimizer and reduces it at the end (your learning rates), and it’s based on that one call. So 2 calls != 1 longer call

3 Likes

Thank you @muellerzr

One more question regarding interpretation:
My model classifies between “Herrenschuhe” (shoes for men) and “Damenschuhe” (shoes for women).
The interp.plot_top_losses function yields the following output:

It has an overall accuracy of 97% (0.97).

Please tell me if i am correct, I interpret the above picture in this matter:

The first shoe was predicted as a “Herrenschuh” but actually is a “Damenschuh”. It has the highest loss within my model of 5.93. The loss is so high that the corresponding probability is 0. At this point the model just took a guess and predicted a “Herrenschuh” but lost the coin flip in this case.

The second shoe was predicted as a “Damenschuh” but actually is a “Herrenschuh”. It has a high loss of 2.55 which corresponds to a probability of 8%. The model is 8% certain that its prediction is “good”.

The third shoe was predicted as a “Herrenschuh” and actually is a “Herrenschuh”. It has the third highest loss of 0.45 in my dataset, that is why its listed even though it was predicted correctly. A loss of 0.45 corresponds to a probability of 64%. The model was 64% certain that its prediction is good. Same thing for the second row.

Please tell me if i am correct or not and why.
Thank you

Yes, you are right.