Today we will see **how normalize data** with PyTorch library and **why is normalization crucial when doing Deep Learning.**

In fact this article is part of a series on **Binary Classification models in PyTorch** with :

- a first part on
**normalization** - a second part on
**Deep Learning models**(available here)

Without further introduction, **let’s begin** this first part on **data normalization.**

**Loading data**

First of all we will** load the data** we need.

We use for that the *datasets* **module**.

It’s **a module** integrated to *PyTorch* that allows to **quickly load datasets**. Ideal **to practice coding !**

**The dataset** that interests us is called *CIFAR*-10. It is composed of **60 000 images in RGB color and size 32×32**; they are divided into **10 classes** (plane, automobile, bird, cat, deer, dog, frog, horse, boat, truck), with **6 000 images per class.**

```
from torchvision import datasets
from torchvision import transforms
data_path = '../data-unversioned/p1ch7/'
cifar10 = datasets.CIFAR10(
data_path, train=True, download=True,
transform=transforms.ToTensor()
)
```

Several parameters are specified:

**data_path**, the directory where the cifar-10 dataset will be saved**train = True**, create the dataset from the training set, if False create from the test set.**download = True**, downloads the dataset from the internet and places it in the root directory. If the dataset is already downloaded, it is not downloaded again.**transform = transforms.ToTensor()**, allows to initialize the images directly as a PyTorch Tensor (if nothing is specified the images are in PIL.Image format)

## Verifying the data

Let’s be a bit more precise, we have a variable *cifar10* which is **a dataset containing tuples.**

**These tuples** are composed of :

**a tensor**(which represents the image)**an int**which represents the label of the image

```
img_t, index_label = cifar10[5]
type(img_t), type(index_label)
```

We have recovered one of the images of the dataset,** let’s display it !**

We recall that an image tensor is in the format **Color X Height X Width**. To display the image, it is necessary to **change its format to Height X Width X Color**.

To do so, we use the *permute()* **function**.

```
import matplotlib.pyplot as plt
plt.imshow(img_t.permute(1, 2, 0))
plt.show()
```

We also may display t**he label associated with the image:**

`index_label`

The *index_label* **variable is equal to 1.** In fact we have retrieved the index that will allow us to know **the name of the label.**

For that, we just have to **refer to this list :**

```
label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
label_names[index_label]
```

Our image has the label ‘automobile’. **So far, everything seems to be consistent !**

**Normalizing data**

**Normalizing** data is a step often forgotten by **Data Scientists**, even though it is essential to build **a good Machine Learning algorithm.**

**Normalization** is the fact of **modifying the data of each channel/tensor** so that** the mean is zero and the standard deviation is one.**

We show you **an example** with **the normalization of a list below :**

We show you an example below with **the normalization of a list below…**

…first, we calculate **the mean** and **the standard deviation :**

```
import numpy as np
l = [60, 9, 37, 14, 23, 4]
np.mean(l), np.std(l)
```

We obtain : *(24.5, 19.102792117035317)*

In fact, this calculation will allow us to apply the following **normalization formula** on each element of the list:

*(element – mean) / standard deviation*

```
l_norm = [(element - np.mean(l)) / np.std(l) for element in l]
print(l_norm)
```

We obtain : *[1.86, -0.81, 0.65, -0.55, -0.08, -1.07]*

Our list is now **normalized**.

We can **check** that **the mean** is 0 and **the standard deviation** is 1:

`np.mean(l_norm), np.std(l_norm)`

We obtain : *(0.0, 1.0)*

**But why do we want to normalize our data?**

In fact there are **two main reasons :**

**normalizing data**includes them in the same range as our activation functions, usually between 0 and 1. This allows for less frequent non-zero gradients during training, and therefore**the neurons in our network will learn faster.****by normalizing each channel**so that they have the same distribution, we ensure that**the channel information can be mixed**and**updated during the gradient descent**(back propagation)**using the same learning rate.**

**Reminder :** we call a **channel** a group of tensor. In our case each image corresponds to a tensor.

**The PyTorch advantage**

### Normalize Data Manually

With *PyTorch* we can **normalize** our data set quite **quickly.**

We are going to create **the tensor channel** we talked about in the previous part.

To do this, we use the *stack()* function by indicating **each of the tensors in our cifar10 variable :**

```
import torch
imgs = torch.stack([img_t for img_t, _ in cifar10], dim=3)
imgs.shape
```

We obtain a channel that contains **50 000 images in 3x32x32 format.**

In fact **this channel is a tensor.** It is a tensor which **contains other tensors** 😉

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Now we can get back to what I was talking about earlier.

Thanks to this channel, we can **calculate the average of all the tensors :**

`imgs.view(3, -1).mean(dim=1)`

We obtain **three mean :** tensor([0.4914, 0.4822, 0.4465])

Each one represents **the mean of each color :** R G B.

Same thing for **the standard deviation :**

`imgs.view(3, -1).std(dim=1)`

We obtain **three standard deviations :** tensor([0.2470, 0.2435, 0.2616])

No need to rewrite the normalization formula, **the** *PyTorch* **library takes care of everything!**

We simply use the *Normalize()* **function** of the *transforms* module by indicating **the mean and the standard deviation :**

`norm = transforms.Normalize((0.4915, 0.4823, 0.4468), (0.2470, 0.2435, 0.2616))`

We can then **normalize an image…**

`out = norm(img_t)`

… or **all images of the channel** at the same time:

`imgs_norm = torch.stack([norm(img_t) for img_t, _ in cifar10], dim=3)`

Finally we can **verify** that our channel is well normalized with **a mean of 0 and a standard deviation of 1 :**

`print(imgs_norm.mean(), imgs_norm.std())`

### Normalize Data Automatical**ly**

**ly**

**If we know the mean and the standard deviation** we can **directly apply the normalization** when loading the tensors.

You just have to add the *Normalize()* function **when we initialize the dataset** as follows:

```
transformed_cifar10 = datasets.CIFAR10(
data_path, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4915, 0.4823, 0.4468),
(0.2470, 0.2435, 0.2616))
]))
```

As you can see, if you want **to call the ***transforms*** module several times** on an object you have **to group these calls **in the *Compose()* function

The *Compose()* **function** allows you to perform **several transformations at the same time.**

**Denormalizing** Data

**Denormalizing**

So we have **our normalized dataset** ready to be used… but before that **let’s display our normalized image** to see what it looks like:

```
import matplotlib.pyplot as plt
img, ind = transformed_cifar10[12]
plt.imshow(img.permute(1, 2, 0))
plt.show()
```

**The image is quite unintelligible…** in addition to being in 32×32, the colors do not look normal.

Actually, **it is normal !**

Following the normalization **the pixels of each image **(of each tensor)** have been modified.**

But then how do we do if we want **to check our images after normalization ?**

Well, you just have **to go back**, to **denormalize.**

To do this we just need to **use these formulas:**

*mean = – mean / standard deviation*

*standard deviation = 1 / standard deviation*

We can **apply this formula directly** with the *Normalize()* function as follows:

```
unorm = transforms.Normalize(mean=[-0.4915/0.2470, -0.4823/0.2435, -0.4468/0.2616],
std=[1/0.2470, 1/0.2435, 1/0.2616])
```

This gives us **an image in due form :**

```
plt.imshow(unorm(img).permute(1, 2, 0))
plt.show()
```

**Prior to Deep Learning**

Let’s keep in mind our main objective: **the Binary classification model.**

We already have **the training data**, now we will load **the validation data **with the* CIFAR10()* function and by indicating *train=False* :

```
transformed_cifar10_val = datasets.CIFAR10(
data_path, train=False, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4915, 0.4823, 0.4468),
(0.2470, 0.2435, 0.2616))
]))
```

In our dataset there are **10 classes.**

We want to do **binary classification**, so we will **keep only 2 of these classes :** deer and horse.

Our **Deep Learning** model will learn to detect **these two classes on images.**

We extract **the images corresponding to these classes** from our dataset :

```
label_map = {4: 0, 7: 1}
class_names = ['deer', 'horse']
cifar2 = [(img, label_map[label])
for img, label in transformed_cifar10
if label in [4, 7]]
cifar2_val = [(img, label_map[label])
for img, label in transformed_cifar10_val
if label in [4, 7]]
```

Finally we **display one of the images of the class ‘deer’ :**

```
img, ind = cifar2[90]
plt.imshow(unorm(img).permute(1, 2, 0))
plt.show()
print('classe : ', class_names[ind])
```

**It seems that we are on the right path !**

We can continue to the second part of this article with **the creation of our Binary Classification model in PyTorch.**

**sources **:

- L. Antiga,
*Deep Learning with PyTorch*(2020, Manning Publications) : - Photo by Diana Parkhouse on Unsplash

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