One of the most common problems of data science professionals is to avoid over-fitting. Have you come across a situation when your model is performing very well on the training data but is unable to predict the test data accurately. The reason is your model is overfitting. The solution to such a problem is regularization. Here in this article you will learn What is Batch Normalization?

*Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.*

The regularization techniques help to improve a model and allows it to converge faster. We have several regularization tools at our end, some of them are early stopping, dropout, weight initialization techniques, and batch normalization in cnn. The regularization helps in preventing the over-fitting of the model and the learning process becomes more efficient.

In this article, you will learn about batch normalization, also referred to as batch normalisation, and its significance in deep learning. We will explore how batch normalisation in deep learning enhances model performance, stabilizes training, and accelerates convergence.

- Understand what batch normalization is and why it is needed in deep neural networks
- Learn how batch normalization works, including the steps of normalization and rescaling/offsetting
- Explore the different techniques of batch normalization and their impact

Before entering into Batch normalization let’s understand the term “Normalization”.

Normalization is a data pre-processing tool used to bring the numerical data to a common scale without distorting its shape.

Generally, when we input the data to a machine or deep learning algorithm we tend to change the values to a balanced scale. The reason we normalize is partly to ensure that our model can generalize appropriately.

Now coming back to Batch normalization, it is a process to make neural networks faster and more stable through adding extra layers in a deep neural network. The new layer performs the standardizing and normalizing operations on the input of a layer coming from a previous layer.

But what is the reason behind the term “Batch” in batch normalization? A typical neural network is trained using a collected set of input data called **batch**. Similarly, the normalizing process in batch normalization takes place in batches, not as a single input.

Let’s understand this through an example, we have a deep neural network as shown in the following image.

Initially, our inputs X1, X2, X3, X4 are in normalized form as they are coming from the pre-processing stage. When the input passes through the first layer, it transforms, as a sigmoid function applied over the dot product of input X and the weight matrix W. Similarly, this transformation will take place for the second layer and go till the last layer L as shown in the following image.

Although, our input X was normalized with time the output will no longer be on the same scale. As the data go through multiple layers of the neural network and L activation functions are applied, it leads to an internal co-variate shift in the data.

Since by now we have a clear idea of why we need Batch Normalization in CNN, let’s understand how it works. It is a two-step process. First, the input is normalized, and later rescaling and offsetting is performed.

Normalization is the process of transforming the data to have a mean zero and standard deviation one. In this step we have our batch input from layer h, first, we need to calculate the mean of this hidden activation.

Here, m is the number of neurons at layer h.

Once we have meant at our end, the next step is to calculate the standard deviation of the hidden activations.

Further, as we have the mean and the standard deviation ready. We will normalize the hidden activations using these values. For this, we will subtract the mean from each input and divide the whole value with the sum of standard deviation and the smoothing term (*Îµ*).

The smoothing term(*Îµ*) assures numerical stability within the operation by stopping a division by a zero value.

In the final operation, the re-scaling and offsetting of the input take place. Here two components of the BN algorithm come into the picture, Îł(gamma) and Î˛ (beta). These parameters are used for re-scaling (Îł) and shifting(Î˛) of the vector containing values from the previous operations.

These two are learnable parameters, during the training neural network ensures the optimal values of Îł and Î˛ are used. That will enable the accurate normalization of each batch.

Batch normalization is a technique used in deep learning that helps our models learn and adapt quickly. Itâ€™s like a teacher who helps students by breaking down complex topics into simpler parts.

Imagine youâ€™re trying to hit a moving target with a dart. It would be much harder than hitting a stationary one, right? Similarly, in deep learning, our target keeps changing during training due to the continuous updates in weights and biases. This is known as the â€śinternal covariate shiftâ€ť. Batch normalization helps us stabilize this moving target, making our task easier.

Batch normalization works by normalizing the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation. However, these normalized values may not follow the original distribution. To tackle this, batch normalization introduces two learnable parameters, gamma and beta, which can shift and scale the normalized values.

**Speeds up learning**: By reducing internal covariate shift, it helps the model train faster.**Regularizes the model**: It adds a little noise to your model, and in some cases, you might not even need to use dropout or other regularization techniques.**Allows higher learning rates**: Gradient descent usually requires small learning rates for the network to converge. Batch normalization helps us use much larger learning rates, speeding up the training process.

Now let’s look into the advantages the BN process offers.

By Normalizing the hidden layer activation the Batch Normalization in CNN speeds up the training process.

It solves the problem of internal covariate shift. Through this, we ensure that the input for every layer is distributed around the same mean and standard deviation. If you are unaware of what is an internal covariate shift, look at the following example.

Suppose we are training an image classification model, that classifies the images into Dog or Not Dog. Let’s say we have the images of white dogs only, these images will have certain distribution as well. Using these images model will update its parameters.

later, if we get a new set of images, consisting of non-white dogs. These new images will have a slightly different distribution from the previous images. Now the model will change its parameters according to these new images. Hence the distribution of the hidden activation will also change. This change in hidden activation is known as an internal covariate shift.

However, according to a study by MIT researchers, the batch normalization does not solve the problem of internal covariate shift.

In this research, they trained three models

Model-1: standard VGG network without batch normalization.

Model-2: Standard VGG network with batch normalization.

Model-3: Standard VGG with batch normalization and random noise.

This random noise has non-zero mean and non -unit variance and added after the batch normalization layer. This experiment reached two conclusions.

- The third model has a less stable distribution across all layers. We can see the noisy model has a high variance than the other two models.

The second conclusion was the training accuracy of the second and third models is higher than the first model. So it can be concluded that internal co-variate shift might not be a contributing factor in the performance of the batch normalization.

Batch normalization smoothens the loss function that in turn by optimizing the model parameters improves the training speed of the model.

This topic, batch normalization is of huge research interest and a large number of researchers are working around it. If you are looking for further details on this, I will recommend you to go through the following links.

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

How Does Batch Normalization Help Optimization?

In TensorFlow, Batch Normalization can be easily implemented using theÂ `tf.keras.layers.BatchNormalization`

Â layer. Hereâ€™s a simple example of how to use it in a model:

```
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, BatchNormalization, Conv2D, MaxPooling2D
model = Sequential([
Conv2D(32, (3, 3), input_shape=(28, 28, 1), activation='relu'),
BatchNormalization(),
Conv2D(64, (3, 3), activation='relu'),
BatchNormalization(),
MaxPooling2D(),
Dense(10, activation='softmax')
])
```

In this example, Batch Normalization is applied after convolutional layers, which is a common practice to help stabilize the training of the model

To summarize, in this article we saw what is Batch Normalization and how it improves the performance of a neural network. Although, we need not perform all this manually as the deep learning libraries like PyTorch and TensorFlow takes care of the complexities in the implementation. Still, being a data scientist it is worth understanding the intricacies of the back-end.

Hope you will find that batch normalization, or batch normalisation, significantly enhances your deep learning models by stabilizing training and accelerating convergence.

- Batch normalization helps prevent overfitting and speeds up training of deep neural networks
- It normalizes activations of each layer by subtracting mean and dividing by standard deviation
- Rescaling and offsetting is done using learnable parameters gamma and beta
- Batch normalization handles internal covariate shift and smoothens the loss landscape

A. Use batch normalization when training deep neural networks to stabilize and accelerate learning, improve model performance, and reduce sensitivity to network initialization and learning rates.

A. Normalization scales input data to a standard range, like 0 to 1. BatchNormalization normalizes intermediate layers’ activations during training, adjusting mean and variance to improve convergence.

A. In Keras, batch normalization standardizes the inputs of each layer to have a mean of zero and variance of one, thus stabilizing and accelerating the training process.

A. Batch normalization acts as a regularization method by reducing overfitting. It introduces noise through mini-batch statistics, which provides a slight regularizing effect similar to dropout.

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Let us know if you have any queries in the comments below.

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