# How GoogleNet actually deal with reducing overfitting?

Today I was going through a tutorial of Andrew Ng about Inception network. He said that GoogLeNet's hidden layers are also good in prediction and it had somehow a regularization effect, so it reduces overfitting. I also search on this topic and tried to figure out by reading the GoogLeNet paper. But I am not satisfied.

• Course: Convolutional Neural Network, Vendor: deeplearning.ai, Website: coursera.org, Course Timestamp: 2nd week (InceptionNet) – Mahir Mahbub Nov 29 '19 at 21:59
• Is this the video? – nbro Nov 29 '19 at 22:37

The main reason of overfitting in any neural network is having too many unrestricted trainable degrees of freedom in the model. Methods similar to dropout reduce the number of neurons at each training run which effectively means having a smaller network. On the other hand in $$l_1$$ and $$l_2$$ regularization, a term added to the loss function which put a constraint on the total loss calculated at each run. So what we are trying to minimize with such regularizations is not just $$L$$, but $$L +l_1*f(w)$$ (for example).
$$L = 0.7*L_9+ 0.3*(L_6+L_3).$$
Here $$L_3$$ and $$L_6$$ represent the losses calculated at first and second outputs, and $$L_9$$ is the loss calculated at the final output of the network.