Is the accuracy and loss in CNN over-fitting?

Is the accuracy and loss in CNN over-fitting?

– Data Science Stack Exchange Accuracy and loss don’t change in CNN. Is it over-fitting? My task is to perform classify news articles as Interesting [1] or Uninteresting [0].

How does loss increase while accuracy stays the same?

This is the classic ” loss decreases while accuracy increases ” behavior that we expect. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). This leads to a less classic ” loss increases while accuracy stays the same “.

What’s the accuracy of the CNN machine learning model?

The model predicts all articles in the validation set as Uninteresting [0]. The accuracy is 97.44% which is same as the ratio of Uninteresting articles in the validation set. I have tried variations of this architecture but still, the issue exists.

Why does the accuracy of TensorFlow keep decreasing?

However, we are now evaluating our model with a validation set during training (on a separate GPU), and it seems like the precision stopped increasing after about 6.7k steps, while the loss is still dropping steadily after over 40k steps. Is this due to overfitting?

How to tackle the problem of constant Val accuracy in CNN model training?

1. Reduce network complexity 2. Use drop out ( more dropout in last layers) 3. Regularise 4. Use batch norms 5. Increase the tranning dataset size. I agree with Mohammad Deeb. This link is useful: https://stackoverflow.com/questions/52356068/validation-accuracy-constant-in-keras-cnn-for-multiclass-image-classification

How much accuracy can you get with CNN?

I have started with Machine Learning recently, I am learning CNN, I planned to write an application for Car Damage severity detection, with the help of this Keras blog and this github repo. Following code gives me only 32% of accuracy.

Why is CNN accuracy not improving with Keras?

Any idea what I’m missing. As in the github repo we can see, it gives 72% accuracy for the same dataset (Training -979, Validation -171). Why its not working for me. I tried his code from the github link on my machine but it hanged up while training the dataset (I waited for more than 8 hours), so changed the approach, but still no luck so far.