Batch Normalization in Convolutional Neural Network

Question

I am newbie in convolutional neural networks and just have idea about feature maps and how convolution is done on images to extract features. I would be glad to know some details on applying batch normalisation in CNN.

I read this paper https://arxiv.org/pdf/1502.03167v3.pdf and could understand the BN algorithm applied on a data but in the end they mentioned that a slight modification is required when applied to CNN:

For convolutional layers, we additionally want the normalization to obey the convolutional property – so that different elements of the same feature map, at different locations, are normalized in the same way. To achieve this, we jointly normalize all the activations in a mini- batch, over all locations. In Alg. 1, we let B be the set of all values in a feature map across both the elements of a mini-batch and spatial locations – so for a mini-batch of size m and feature maps of size p × q, we use the effec- tive mini-batch of size m′ = |B| = m · pq. We learn a pair of parameters γ(k) and β(k) per feature map, rather than per activation. Alg. 2 is modified similarly, so that during inference the BN transform applies the same linear transformation to each activation in a given feature map.

I am total confused when they say "so that different elements of the same feature map, at different locations, are normalized in the same way"

I know what feature maps mean and different elements are the weights in every feature map. But I could not understand what location or spatial location means.

I could not understand the below sentence at all "In Alg. 1, we let B be the set of all values in a feature map across both the elements of a mini-batch and spatial locations"

I would be glad if someone cold elaborate and explain me in much simpler terms

Answer 1

Let's start with the terms. Remember that the output of the convolutional layer is a 4-rank tensor [B, H, W, C], where B is the batch size, (H, W) is the feature map size, C is the number of channels. An index (x, y) where 0 <= x < H and 0 <= y < W is a spatial location.

Usual batchnorm

Now, here's how the batchnorm is applied in a usual way (in pseudo-code):

# t is the incoming tensor of shape [B, H, W, C]
# mean and stddev are computed along 0 axis and have shape [H, W, C]
mean = mean(t, axis=0)
stddev = stddev(t, axis=0)
for i in 0..B-1:
  out[i,:,:,:] = norm(t[i,:,:,:], mean, stddev)

Basically, it computes H*W*C means and H*W*C standard deviations across B elements. You may notice that different elements at different spatial locations have their own mean and variance and gather only B values.

Batchnorm in conv layer

This way is totally possible. But the convolutional layer has a special property: filter weights are shared across the input image (you can read it in detail in this post). That's why it's reasonable to normalize the output in the same way, so that each output value takes the mean and variance of B*H*W values, at different locations.

Here's how the code looks like in this case (again pseudo-code):

# t is still the incoming tensor of shape [B, H, W, C]
# but mean and stddev are computed along (0, 1, 2) axes and have just [C] shape
mean = mean(t, axis=(0, 1, 2))
stddev = stddev(t, axis=(0, 1, 2))
for i in 0..B-1, x in 0..H-1, y in 0..W-1:
  out[i,x,y,:] = norm(t[i,x,y,:], mean, stddev)

In total, there are only C means and standard deviations and each one of them is computed over B*H*W values. That's what they mean when they say "effective mini-batch": the difference between the two is only in axis selection (or equivalently "mini-batch selection").

Answer 2

Some clarification on Maxim's answer.

I was puzzled by seeing in Keras that the axis you specify is the channels axis, as it doesn't make sense to normalize over the channels - as every channel in a conv-net is considered a different "feature". I.e. normalizing over all channels is equivalent to normalizing number of bedrooms with size in square feet (multivariate regression example from Andrew's ML course). This is usually not what you want - what you do is normalize every feature by itself. I.e. you normalize the number of bedrooms across all examples to be with mu=0 and std=1, and you normalize the the square feet across all examples to be with mu=0 and std=1.

This is why you want C means and stds, because you want a mean and std per channel/feature.

After checking and testing it myself I realized the issue: there's a bit of a confusion/misconception here. The axis you specify in Keras is actually the axis which is not in the calculations. i.e. you get average over every axis except the one specified by this argument. This is confusing, as it is exactly the opposite behavior of how NumPy works, where the specified axis is the one you do the operation on (e.g. np.mean, np.std, etc.).

I actually built a toy model with only BN, and then calculated the BN manually - took the mean, std across all the 3 first dimensions [m, n_W, n_H] and got n_C results, calculated (X-mu)/std (using broadcasting) and got identical results to the Keras results.

Hope this helps anyone who was confused as I was.

Answer 3

I'm only 70% sure of what I say, so if it does not make sense, please edit or mention it before downvoting.

About location or spatial location: they mean the position of pixels in an image or feature map. A feature map is comparable to a sparse modified version of image where concepts are represented.

About so that different elements of the same feature map, at different locations, are normalized in the same way: some normalisation algorithms are local, so they are dependent of their close surrounding (location) and not the things far apart in the image. They probably mean that every pixel, regardless of their location, is treated just like the element of a set, independently of it's direct special surrounding.

About In Alg. 1, we let B be the set of all values in a feature map across both the elements of a mini-batch and spatial locations: They get a flat list of every values of every training example in the minibatch, and this list combines things whatever their location is on the feature map.

Answer 4

1. Firstly we need to make it clear that the depth of a kernel is determined by previous feature map's channel num, and the number of kernel in this layer determins the channel num of next feature map (the next layer).
2. then we should make it clear that each kernel(three dimentional usually) will generate just one channel of feature map in the next layer.
3. thirdly we should try to accept the idea of each points in the generated feature map (regardless of their position) are generated by the same kernel, by sliding on previous layer. So they could be seen as a distribution generated by this kernel, and they could be seen as samples of a stochastic variable. Then they should be averaged to obtain the mean and then the variance. (it not rigid, only helps to understand) This is what they say "so that different elements of the same feature map, at different locations, are normalized in the same way"