Contents
Why is CNN Overfitting?
Overfitting indicates that your model is too complex for the problem that it is solving, i.e. your model has too many features in the case of regression models and ensemble learning, filters in the case of Convolutional Neural Networks, and layers in the case of overall Deep Learning Models.
Can a neural network Overfit?
One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations.
What causes overfitting in a deep neural network?
One of the most common problems that I encountered while training deep neural networks is overfitting. Overfitting occurs when a model tries to predict a trend in data that is too noisy. This is the caused due to an overly complex model with too many parameters.
What can you do with a convolutional neural network?
Especially in image processing we have a wide range of possibilities in that area. We can slightly rotate, move, change its colors or make other more or less subtle changes that will give the model tons of new data.
When to take measures against overfitting in deep learning?
As a general rule: Start by overfitting the model, then take measures against overfitting. Overfitting happens when your model fits too well to the training set. It then becomes difficult for the model to generalize to new examples that were not in the training set.
Why is the convolutional network not densely connected?
The convolutional network is not densely connected and we should rather try to increase its complexity rather than reduce it, because it will be able to capture more features of the image. Therefore, to reduce the complexity of architecture, it is good idea to start with the densely connected part in the first place.