# When should one use L1, L2 regularization instead of dropout layer, given that both serve same purpose of reducing overfitting?

In Keras, there are 2 methods to reduce over-fitting. L1,L2 regularization or dropout layer. https://keras.io/regularizers/

https://keras.io/layers/core/#dropout

What are some situations to use L1,L2 regularization instead of dropout layer? What are some situations when dropout layer is better?

• L1, L2 regularizers don't really work in NN's if you are using an NN..A good intuition is given by lec 2/3/4 (I am not sure) of Stanford CNN course.. Just serach it on utube – DuttaA Aug 23 '18 at 17:36

$L_1$ versus $L_2$ is easier to explain, simply by noting that $L_2$ treats outliers a little more thoroughly - returning a larger error for those points. Have a look here for more detailed comparisons.
• If your $L2$ loss increases, so would your $L1$ loss (just perhaps more slowly). Either way, your model is diverging from a minimum in the loss curve i.e. it isn't learning. You might want to think about other parts of your model again, such as the architecture, data preprocessing or class-imbalance in your data. Those keywords might help you in the right direction with some searches. – n1k31t4 Sep 5 '18 at 13:15