Should batch norm before or after ReLU?

Should batch norm before or after ReLU?

So the Batch Normalization Layer is actually inserted right after a Conv Layer/Fully Connected Layer, but before feeding into ReLu (or any other kinds of) activation.

What is the point of Batch Normalization?

Batch normalization is a technique to standardize the inputs to a network, applied to ether the activations of a prior layer or inputs directly. Batch normalization accelerates training, in some cases by halving the epochs or better, and provides some regularization, reducing generalization error.

When do you use Relu for input normalization?

When you use ReLU because there is no limit for its output, you have to normalize the input data and also use initialization techniques that avoid having large values for weights. For more information I encourage you taking a look at here and here.

Where does batch normalization occur in a neural network?

Typically, batch normalization is found in deeper convolutional neural networks such as Xception, ResNet50 and Inception V3. The neural network implemented above has the Batch Normalization layer just before the activation layers. But it is entirely possible to add BN layers after activation layers.

When to use input normalization in machine learning?

Let’s assume a vanilla MLP for classification with a given activation function for hidden layers. I know it is a known best practice to normalize the input of the network between 0 and 1 if sigmoid is the activation function and -0.5 and 0.5 if tanh is the activation function. What about ReLu ?

Can you add BN layers after batch normalization?

The neural network implemented above has the Batch Normalization layer just before the activation layers. But it is entirely possible to add BN layers after activation layers. There has been some extensive work done by researchers on the Batch Normalization technique. For example Batch Renormalization and Self Normalizing Neural Networks