EDUCBA

EDUCBA

MENUMENU
  • Blog
  • Free Courses
  • All Courses
  • All in One Bundle
  • Login
Home Software Development Software Development Tutorials PyTorch Tutorial PyTorch Neural Network

PyTorch Neural Network

Updated April 7, 2023

PyTorch Neural Network

Definition of PyTorch Neural Network

Basically, PyTorch is a framework that is used to implement deep learning, the neural network consists of the different types of layers and modules and with help of those layers and modules, we can perform the different operations on data as per requirement. In PyTorch, every module has a subclass that is nn.Module. The neural network has a different module that means we can say that it has the sub-layers or modules themselves. So with the help of this nested structure of the neural network, we can easily implement some complex architecture as per our requirement. For implementation purposes, we need to use a torch.nn package.

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

What is PyTorch neural network?

Neural networks at their center are simply one more instrument in the arrangement of AI calculations. Neural network comprises of a lot of “neurons” which are values that get going as your feedback information, and afterward get increased by loads, added together, and afterward went through an actuation capacity to create new qualities, and this interaction then, at that point, rehashes over anyway many “layers” your neural network needs to then deliver a yield.

In secret layers (“stowed away” just for the most part alludes to the way that the developer doesn’t actually set or control the qualities to these layers, the machine does), these are neurons, numbering in any way numerous we need, and afterward they lead to a yield layer. The yield is normally either a single neuron for relapse undertakings or however many neurons as you have classes.
Whichever neuron has the most elevated worth, is the anticipated class. So, perhaps the highest point of the three yield neurons is “human,” then, at that point “canine” in the center and afterward “feline” on the base. Assuming the human worth is the biggest one, then, at that point, that would be the forecast of the neural network.

How to use code neural network?

Now let’s see how we can use code in a neural network as follows. In the above point, we already discussed what a neural network is. Now let’s see the example as follows.

First, we need to import the required package as follows.

import torch
import torch. nn as nn
import torch.nn.functional as Fun

After that we need to create the network by using the following code as follows.

class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.conv = nn.Conv2d(2, 4, 4)
        self.conv = nn.Conv2d(4, 12, 3)
        self.fu1 = nn.Linear(10 * 4 * 3, 90) # 5*5 from image dimension
        self.fu2 = nn.Linear(90, 70)
        self.fu3 = nn.Linear(60, 12)
    def next(self, A):
        A = Fun.max_pool2d(Fun.relu(self.conv(A)), (3, 3))
        A = Fun.max_pool2d(Fun.relu(self.conv2d(A)), 3)
        A = torch.flatten(A, 2) # flatten all dimensions except the batch dimension
        A = Fun.relu(self.fu1(A))
        A = Fun.relu(self.fu2(A))
        A = self.fu3(A)
        return A
net_value = Network()
print(net_value)

Explanation

In the above example, we try to implement the neural network; here first we create the network module as shown. Here we consider single input and four output channels as per our requirement. After that, we need to create another function for the pooling of the window as shown in the above code. After execution of the above code, it shows features of input as well as features of outputs. The final result of the above program we illustrated by using the following screenshot as follows.

11

Typical PyTorch neural network

PyTorch gives richly planned modules and classes, including torch.nn, to assist you with making and training neural organizations. An nn.Module contains layers and a technique forward (input) that profits the yield.

Alternatively, you might need to be running things on a GPU, rather than our CPU.

GPU

We regularly need to run on the GPU on the grounds that what we do with these tensor-handling libraries is process colossal quantities of basic computations. “Deeply” our CPU can just do 1 thing? With virtual centers, these copies, however, CPUs were intended to chip away at substantially more confounded, hard-to-tackle, issues all at once. GPUs were expected to assist with producing illustrations, which likewise require some little/straightforward estimation. Accordingly, your CPU most likely does somewhere close to 8 and 24 estimations all at once. A fair GPU will do thousands.

For this instructional exercise, you can in any case track with your CPU, and most likely any CPU will work. For pretty much any down-to-earth use-instance of profound learning, however, you truly will require a decent GPU.

Cloud GPUs

There are a few “free” stages that do offer GPUs on a complementary plan, in any case, once more, this won’t be reasonable for any genuine case, and in the end, you will need to update your record there and afterward, and you will be addressing costs ordinarily above industry standard for what you’re getting. There are no corners to cut, eventually; you will need a high-end GPU locally, or in the cloud.

Implement PyTorch neural network

Now let’s see how we can implement the PyTorch neural network as follows. First, we need to import the required libraries for loading data as shown in the following.

import torch
import torch. nn as nn
import torch.nn.functional as Fun

After that, we need to define the neural network as per our requirement. In PyTorch, we can use convolution for image processing to add the element with all required data as shown in the above example.

In the third step, we need to specify the model that will pass the data by using a training model with different types of function. The function depends on our requirements.

In the next step, we need to pass the data through the model and this is an optional part where we can use the torch. This entire step we can see in the above example.

Conclusion

We hope from this article you learn more about the PyTorch neural network. From the above article, we have taken in the essential idea of the PyTorch r neural network and we also see the representation and example of the PyTorch neural network. From this article, we learned how and when we use the PyTorch neural network.

Recommended Articles

We hope that this EDUCBA information on “PyTorch neural network” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

  1. What is PyTorch?
  2. PyTorch vs Keras
  3. PyTorch Versions
  4. Keras vs TensorFlow vs PyTorch
All in One Excel VBA Bundle
500+ Hours of HD Videos
15 Learning Paths
120+ Courses
Verifiable Certificate of Completion
Lifetime Access
Financial Analyst Masters Training Program
2000+ Hours of HD Videos
43 Learning Paths
550+ Courses
Verifiable Certificate of Completion
Lifetime Access
All in One Data Science Bundle
2000+ Hour of HD Videos
80 Learning Paths
400+ Courses
Verifiable Certificate of Completion
Lifetime Access
All in One Software Development Bundle
5000+ Hours of HD Videos
149 Learning Paths
1050+ Courses
Verifiable Certificate of Completion
Lifetime Access
Primary Sidebar
All in One Software Development Bundle5000+ Hours of HD Videos | 149 Learning Paths | 1050+ Courses | Verifiable Certificate of Completion | Lifetime Access
Financial Analyst Masters Training Program2000+ Hours of HD Videos | 43 Learning Paths | 550+ Courses | Verifiable Certificate of Completion | Lifetime Access
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Java Tutorials
  • Python Tutorials
  • All Tutorials
Certification Courses
  • All Courses
  • Software Development Course - All in One Bundle
  • Become a Python Developer
  • Java Course
  • Become a Selenium Automation Tester
  • Become an IoT Developer
  • ASP.NET Course
  • VB.NET Course
  • PHP Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2023 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

EDUCBA
Free Software Development Course

Web development, programming languages, Software testing & others

By continuing above step, you agree to our Terms of Use and Privacy Policy.
*Please provide your correct email id. Login details for this Free course will be emailed to you

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more