When training any neural network, constant validation accuracy is a common issue because the network just remembers the sample, resulting in an over-fitting problem. Over-fitting a model indicates that the neural network model performs admirably on the training sample, but the model's performance deteriorates on the validation set. Following are some ways for improving CNN's constant validation accuracy:
It is always a good idea to split the dataset into three sections: training, validation, and testing.
When working with limited data, this difficulty can be handled by experimenting with the neural network's parameters.
- By increasing the training dataset's size.
- By using batch normalization.
- By implementing regularization
- By reducing the complexity of the network
