# CrossEntropyLoss getting value > 1

Hey all,
I am training my highly imbalanced sentiment classification dataset using transformers’ library’s ELECTRA(similar to BERT) model by appending a classification head on top of it. The dataset has 5 classes. And I logging the loss every 10 steps.

The imbalance dataset stats are as follows:
The number of 1 labels: 135
The number of 2 labels: 43
The number of 3 labels: 74
The number of 4 labels: 303
The number of 5 labels: 2242

The batch_size I am using is 16.
The

The problem I am facing is the training loss I am encountering is starts from 1.5, which reduces to about 0.7.

Following are the doubts I have:

1. Is it theoretically possible to have loss output from nn.CrossEntropyLoss >1?
2. Also I thought loss output comes in terms of %, so how am I supposed to interpret this loss which starts with greater than 1 value?

its is , it just gives you -(summation (log(p_i))) where p_i is the confidence in the prediction of i’th class , so in theory since natural log function is bound from 0 to - inf between 0 and 1 (since we are taking a negative of that ) the cross entory function is bound between 0 to inf

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adding up to prev answer, use some metric to measure model improvement like accuracy, losses aren’t that easy to introspect, unless it’s MSE imho.

logs are bound to -inf to +inf;

• ln(e^-100000) = -100000
• ln(e^1000000) = 1000000

Yet as @archit-spec indicates, if \hat{y} is in the range 0-1, then the log will be within -inf to 0, and -log within 0 to +inf

First of all, Thanks for the replies.
Also then, what do you all suggest I should Log in the logger to plot, the Cross entropy loss coming from the network , or use calculate precision, recall by hand and plot it? @archit-spec @Mah_Neh

Specifically , I want to report that the model is learning, what loss/metric should I report?

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I tend to log train loss, test loss, and train test accuracies as well. There is some overlap but also they provide different information.

However I do not know what is a standard metric for NLP.

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Oh no, just from categorical/classification point of view u can answer. I am doing simple sentiment analysis which is just classification.
So, I should calculate the accuracy of the predictions using the target(output from model) and predicted labels, is this what you are saying? Please clarify .Thanks again.

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Yes, exactly, either manually or using some torcheval metric (torcheval is a package, utils for evaluating your model.(

You can also calculate the multi / binary class confusion matrix, at least I find it useful to understand what is going on.

I do not think you can include the confusion matrix in Tensorboard though.

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Thank you for the reply, I will try this.