How do I increase my CNN validation accuracy?

We have the following options.

1. Use a single model, the one with the highest accuracy or loss.
2. Use all the models. Create a prediction with all the models and average the result.
3. Retrain an alternative model using the same settings as the one used for the cross-validation. But now use the entire dataset.

Why does validation loss fluctuate?

Your learning rate may be big, so try decreasing it. The size of validation set may be too small, such that small changes in the output causes large fluctuations in the validation error.

What is good accuracy for CNN?

Building CNN Model with 95\% Accuracy | Convolutional Neural Networks.

Why accuracy is not increasing?

If the accuracy is not changing, it means the optimizer has found a local minimum for the loss. This may be an undesirable minimum. One common local minimum is to always predict the class with the most number of data points. You should use weighting on the classes to avoid this minimum.

Why does validation accuracy decrease?

Overfitting happens when a model begins to focus on the noise in the training data set and extracts features based on it. This helps the model to improve its performance on the training set but hurts its ability to generalize so the accuracy on the validation set decreases.

Can validation accuracy be more than training accuracy?

The validation accuracy is greater than training accuracy. This means that the model has generalized fine. If you don’t split your training data properly, your results can result in confusion. so you either have to reevaluate your data splitting method by adding more data, or changing your performance metric.

Can test accuracy be higher than validation accuracy?

1 Answer. Theoretically, it is possible to have a higher test accuracy than the validation accuracy.

Why is my validation accuracy higher than my training accuracy?

The validation and test accuracies are only slightly greater than the training accuracy. This can happen (e.g. due to the fact that the validation or test examples come from a distribution where the model performs actually better), although that usually doesn’t happen. How many examples do you use for validation and testing?$\\endgroup$

How accurate is this model for Face Recognition Validation?

This model achieved a validation accuracy of 58\%. A 4\% achievement, sure, but at the expense of significantly more computational power. In fact, I tried running this model on top of the MTCNN face recognition model, and my computer crashed. There has to be better models out there.

Is the validation accuracy better than a coin toss?

The validation accuracy is not better than a coin toss, so clearly my model is not learning anything. I have tried different values of dropout and L1/L2 for both the convolutional and FC layers, but validation accuracy is never better than a coin toss.

Can over fitting decrease the accuracy of a model?

However when evaluating validation accuracy and test accuracy drop out is NOT active so the model is actually more accurate. This increase in accuracy might be enough to overcome the decrease due to over fitting. Especially possible in this case since the accuracy differences appear to be quite small.