I have recently read about Batch Normalization for Deep Learning online.

Unfortunately, the notation is really inconsistent and confusing, so perhaps someone can help.

Main Question:

Let's assume we have a neural network $\mathcal{N}$ consisiting of $D_{l}$ neurons in the $l$ th hidden layer and a dataset of $N$ samples from some $d$-dimensional space, organized in a matrix $X \in \mathbb{R}^{N \times d}$.

Then, the outputs (activations) of the $(l-1)$th layer are given by a matrix $H_{l-1} \in \mathbb{R}^{N \times D_{l-1}}$.

The input of the $i$th neuron of the next layer is hence the $i$th column of $Y_{l} = H_{l-1}W_{l} + \theta_{l}$ with each entry corresponding to one instance in the dataset.

Now what exactly is being normalized?

I would assume the following: $$\hat Y_{l}^{ij} = \gamma \cdot \frac{Y_{l}^{ij} - \mu_{j}}{\sigma_{j}} + \beta$$ for $\mu_{j} = \frac{1}{N} \cdot \sum_{i=1}^{N} Y_{l}^{ij}$ and $\sigma_{j}$ accordingly.

Is this correct?

Finally, do the scale and offset parameters $\gamma$ and $\beta$ depend on $j$ also, or are they computed for each neuron individually?

Please can someone just give me a formula...


If someone can explain how this arithmetic is extended to the case if our input is a tensor used in image classification where $\dim(X) = (N,C,W,L)$ where $C$ is the number of channels, I would be very grateful, but if not I am also happy.

I usually post on the mathematics-stackexchange but this really seemed to be more appropriate here.


1 Answer 1


I'm guessing that by $j$ you mean the index of the batch, i.e. $j=1$ means 1st batch, right?

What is happening is that each column $i$ gets normalized to zero mean and unit standard deviation and then shifted and scaled by $\beta$ and $\gamma$, accordingly.

This means that since you have $D_{l-1}$ columns in $H_{l-1}$:
$\mu, \sigma, \beta$ and $\gamma$ all will be vectors with $D_{l-1}$ dimensions, the latter two of which are trainable.

Thus the batch normalization operation with input $Y_{l}^{ij}$ and output $\hat Y_{l}^{ij}$ would look like this.

$$\hat Y_{l}^{ij} = \gamma_j \cdot \frac{Y_{l}^{ij} - \mu_{j}}{\sigma_{j}} + \beta_j$$

In image datasets where you have a shape of $(N, H, W, C)$, where $C$ is the number of channels, each of the variables of barchnorm $\mu, \sigma, \beta$ and $\gamma$ would have $C$ dimensions.

We can user keras to confirm this on our own.

1) Tabular data

import tensorflow as tf  # requires tensorflow >= 2.0.0

inp = tf.keras.layers.Input((30,))  # 30 columns (irrelevant to BN)
x = tf.keras.layers.Dense(50)(inp)  # 50 neurons on the first hidden layer
bn = tf.keras.layers.BatchNormalization()(x)  # add batchnorm after hidden layer
out = tf.keras.layers.Dense(5)(bn)  # 5 classes (irrelevant to BN)

model = tf.keras.models.Model(inp, out)

This will print the following:

Layer (type)                 Output Shape              Param #   
input_3 (InputLayer)         [(None, 30)]              0         
dense_2 (Dense)              (None, 50)                1550      
batch_normalization_2 (Batch (None, 50)                200       
dense_4 (Dense)              (None, 5)                 255       
Total params: 2,005
Trainable params: 1,905
Non-trainable params: 100

What interests us is the $200$ parameters that batchnorm has. Why $200$? Because there are $4$ variables (i.e. $\mu, \sigma, \beta$ and $\gamma$), each having $50$ dimensions (i.e. as many as the neurons of the previous layer).

2) Image data

Let's do the same thing on a CNN for image classification.

inp = tf.keras.layers.Input((100, 200, 3))  # height=100px, width=200px, channels=3
c = tf.keras.layers.Conv2D(30, (4, 4), padding='same')(inp)  # same padding to keep the same height/width
bn = tf.keras.layers.BatchNormalization()(c)  # add batchnorm after conv
fl = tf.keras.layers.Flatten()(bn) 
out = tf.keras.layers.Dense(10)(fl)  # 10 classes

model = tf.keras.models.Model(inp, out)

This will print the following:

Layer (type)                 Output Shape              Param #   
input_1 (InputLayer)         [(None, 100, 200, 3)]     0         
conv2d (Conv2D)              (None, 100, 200, 30)      1470      
batch_normalization (BatchNo (None, 100, 200, 30)      120       
flatten (Flatten)            (None, 600000)            0         
dense (Dense)                (None, 10)                6000010   
Total params: 6,001,600
Trainable params: 6,001,540
Non-trainable params: 60

Again we are interested in the $120$ parameters of batchnorm. Why $120$? Because each of the $4$ variables has $C=30$ dimensions.

  • $\begingroup$ Thanks for your answer. To me $Y^{ij}_{l}$ is the matrix-input of the $l$th hidden layer (i.e. the value obtained for the $i$th sample in the dataset at the $j$th neuron of th $l$th layer). So $Y_{l}^{ij} \in \mathbb{R}^{N \times D_{l}}$. I am a bit unsure why you are speaking of $H_{l-1}$ and $D_{l-1}$ now, since I thought what we are talking about is the normalization of the neuron-wise input of the $l$th layer. So I would expect $\mu$ to be a vector of the same length as the number of columns in $Y_{l}^{ij}$, that is I would assume $dim(\mu) = D_{l}$ $\endgroup$ Jan 12, 2021 at 21:28
  • $\begingroup$ I thought that $Y_l$ is the output of the layer and $H_{l-1}$ was its input. I might have misread the notation. If $Y_l$ is the input, then each one of the 4 variables of BN has, as you say, the same number of columns as $Y_l$. $\endgroup$
    – Djib2011
    Jan 12, 2021 at 21:59
  • $\begingroup$ In my notation, $H_{l-1}$ is passed to the $l$th layer, where it is multiplied with $W_{l}$ and added to $\theta_{l}$ to compute $Y_{l}$. Then, the activation function is applied. But as I understand your formula and your example, we would batch normalize $H_{l-1}= \sigma(Y_{l-1})$ instead - I denote $\sigma()$ for the activation here. $\endgroup$ Jan 13, 2021 at 12:51
  • $\begingroup$ Is that correct? $\endgroup$ Jan 13, 2021 at 17:12
  • 1
    $\begingroup$ Yes, this is correct. I mixed up the notation a bit but I hope I helped! $\endgroup$
    – Djib2011
    Jan 13, 2021 at 22:32

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