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A Gentle Introduction to PyTorch Library for Deep Learning

This article was published as a part of the Data Science Blogathon.

The following tutorial assumes some basic knowledge about Python programming language and high school mathematics. No prior knowledge of Deep learning is required. This article covers basic knowledge and working of PyTorch required to get started with Deep Learning. Follow along with the tutorial to get hands-on experience.

PyTorch is an optimized tensor library primarily used for Deep Learning applications using GPUs and CPUs. It is an open-source machine learning library for Python, mainly developed by the Facebook AI Research team. It is one of the widely used Machine learning libraries, others being TensorFlow and Keras. Here is the Google Search Trends which shows that the popularity of the PyTorch library is relatively higher compared to TensorFlow and Keras.

PyTorch Library trend

PyTorch is built based on python and torch library which supports computations of tensors on Graphical Processing Units. Currently is the most favored library for the deep learning and artificial intelligence research community.

Now let’s get hands-on with PyTorch!!

We are using Jupyter Notebooks to run our code. We suggest to follow through the tutorial on Google Colaboratory. It’s a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. We can also be able to use a GPU for free. You can check out this link for some guidance to use Colab.

Tensors

PyTorch is a library for processing tensors. A tensor is a fundamental unit of data. It can be a number, vector, matrix, or any n-dimensional array. It is similar to Numpy arrays.

Before getting started we should import the torch module as follows:

import torch

Creating Tensors

Creating a tensor t1 with a single number as data

# Tensor with Single number
t1 = torch.tensor(5.)
print(t1)
Output : tensor(5.)

5. is shorthand for 5.0. It is used to indicate PyTorch that a tensor is a floating-point number. We can verify the above-using tensor.dtype. If you are using jupyter notebook then you can directly input the variable in the cell and run it to see the results.

print(t1.dtype)
Output: torch.float32

Similarly, we can create tensors of vector type,

# Tensor with 1D vector
t2 = torch.tensor([1, 2, 3., 4])
print(t2)
Output: tensor([1., 2., 3., 4.])

From the above output we can see that even if one of the elements of the vector is a floating-point number, the tensor will convert the data type of all the elements to float.

Now let’s create a 2D tensor,

# Matrix 
t3 = torch.tensor([[1., 2, 3],
                   [4, 5, 6],
                   [7, 8, 9]])
print(t3)
Output: tensor([[1., 2., 3.],
               [4., 5., 6.],
               [7., 8., 9.]])

A 3D tensor,

t4 = torch.tensor([
    [[10. , 11, 12],
     [13, 14, 15]],
    [[16, 17, 18],
     [19, 20, 21]]
])
print(t4)
Output: tensor([[[10., 11., 12.],
         [13., 14., 15.]],
        [[16., 17., 18.],
         [19., 20., 21.]]])

If we observe these tensors are similar to NumPy arrays. So we can check the shape of the the tensors using tensor.shape. The dimension of the array would be the length of the returned shape.

So the shapes of the tensors we defined above would be,

print(t1.shape)
Output: torch.Size([])

As t1 is just a number, its dimension is 0.

print(t2.shape)
Output: torch.Size([4])

As t2 is a vector, its dimension is 1.

print(t3.shape)
Output: torch.Size([3, 3])

As t3 is a matrix of size 3 x 3, its dimension is 2

print(t4.shape)
Output: torch.Size([2, 2, 3])

As t4 is has stacked two tensors of 2 x 3, its dimension is 3

Some Tensor Operations and Computing Gradients

We can perform operations on tensors with the usual arithmetic operations. Also, tensors have a special ability to compute gradients or derivatives of the given expression with respect to all the independent variables. Let’s look at an example,

Define some tensors and then initialize some values,

x = torch.tensor(3.)
w = torch.tensor(4. ,requires_grad=True)
z = torch.tensor(5. ,requires_grad=True)
x , w , z
Output: (tensor(3.), tensor(4., requires_grad=True), tensor(5., requires_grad=True))

In the above code snippet,  we created 3 tensors x, w, and z with numbers and for w and z an additional parameter requires_grad set to True.

So now let’s perform an arithmetic operation with these tensors,

y = x*w + z
print(y)
Output: tensor(17., grad_fn=<AddBackward0>)

So according to basic multiplication and addition we got the output as expected i.e. y = 3 * 4 + 5 = 17.

AutoGrad

So now let us discuss a unique ability of Pytorch which can automatically compute the derivative of any expression (y in this case) wrt to independent variables which have the parameter requires_grad set to True

This can be done by invoking .backward method on y

#Compute derivatives
y.backward()

We can find the derivatives of y wrt input tensors stored in .grad properties of respective input tensors.

print("dy/dx =", x.grad)
print("dy/dw =", w.grad)
print("dy/dz =", z.grad)
Output : dy/dx =  None
         dy/dw =  tensor(3.)
         dy/dz =  tensor(1.)

We can observe the following:

  •  the value of the derivative of y wrt x is None as the parameter requires_grad is set to False
  • the value of the derivative of y wrt w is 3 as dy/dw = x = 3
  • the value of the derivative of y wrt  z is 1 as dy/dz = 1

PyTorch with NumPy

NumPy is a popular open-source library used for scientific and mathematical computing in python. It also supports operations on large multi-dimensional arrays and computations based on linear algebra, Fourier transforms, and matrices. NumPy has a vast ecosystem of supporting libraries including Pandas, Matplotlib, and OpenCv.

So PyTorch interoperates with NumPy to leverage the tools and libraries of NumPy and then extend the capabilities further.

First, let’s create a NumPy array.

#First create a numpy array 
import numpy as np
x  = np.array([1, 2., 3])
print(x)
Output: array([1., 2., 3.])

So we can convert the Numpy arrays to Torch tensors using torch.from_numpy()

#Create a tensor from numpy array
y. = torch.from_numpy(x)
print(y)
Output: tensor([1., 2., 3.], dtype=torch.float64)

We can check the data types using .dtype

print(x.dtype)
print(y.dtype)
Output: float64
        torch.float64

Now we can convert the PyTorch tensors to NumPy arrays using .numpy() method

z = y.numpy()
print(z)
Output: array([1., 2., 3.])

The interoperability with NumPy is required because most of the datasets with which you will be working will most likely be processed in NumPy.

Here you might wonder why we are using Pytorch instead of NumPy since it also provides all the required libraries and utilities required for working with multi-dimensional arrays and perform large calculations. It is mainly for two reasons:

  • AutoGrad: The ability to compute gradients for tensor operations is a powerful ability that is essential for training neural networks and perform backpropagation.
  • GPU Support: While working with massive datasets and large models, PyTorch tensor operations are carried out in Graphical Processing Units (GPUs), which will reduce the amount of time takes by ordinary CPUs by 40x to 50x.
Hence PyTorch is a very powerful Deep learning library widely used in the research and development community for its expressivity and pythonic nature.

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