The Project | About Us | Contribute | Donations | License
HOME My courses Completed
◄ Previous
◄

Custom Project

End!

In this final module, you will apply everything you've learned throughout the course to create a custom project of your choice. This project will help you solidify your understanding of PyTorch and demonstrate your ability to build and train neural networks for real-world applications.

Project Planning

  1. Define the Problem

  • Objective: Clearly state the problem you want to solve.
  • Data: Identify the dataset you will use. Ensure it is relevant to your problem.
  • Outcome: Define what success looks like for your project. What metrics will you use to evaluate your model?

  1. Data Collection and Preprocessing

  • Data Sources: List the sources from where you will collect your data.
  • Data Cleaning: Describe the steps you will take to clean and preprocess the data.
  • Data Augmentation: If applicable, explain how you will augment your data to improve model performance.

  1. Model Selection

  • Model Type: Choose the type of neural network that best suits your problem (e.g., CNN, RNN, GAN).
  • Architecture: Outline the architecture of your model, including the number of layers, types of layers, and activation functions.

  1. Training and Evaluation

  • Training Loop: Describe the training loop, including the loss function and optimizer you will use.
  • Validation: Explain how you will validate your model during training.
  • Testing: Describe how you will test your model and the metrics you will use to evaluate its performance.

  1. Deployment

  • Deployment Strategy: If applicable, describe how you will deploy your model for real-world use.
  • Tools and Platforms: List the tools and platforms you will use for deployment (e.g., Flask, AWS, Heroku).

Example Project: Image Classification

  1. Define the Problem

  • Objective: Classify images of handwritten digits (0-9) from the MNIST dataset.
  • Data: Use the MNIST dataset, which contains 60,000 training images and 10,000 test images.
  • Outcome: Achieve an accuracy of at least 98% on the test set.

  1. Data Collection and Preprocessing

  • Data Sources: The MNIST dataset is available through PyTorch's torchvision library.
  • Data Cleaning: Normalize the pixel values to be between 0 and 1.
  • Data Augmentation: Apply random rotations and shifts to the training images.
import torch
import torchvision
import torchvision.transforms as transforms

# Define transformations
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,)),
    transforms.RandomRotation(10),
    transforms.RandomAffine(0, translate=(0.1, 0.1))
])

# Load the dataset
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

  1. Model Selection

  • Model Type: Convolutional Neural Network (CNN)
  • Architecture: Two convolutional layers followed by two fully connected layers.
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.fc1 = nn.Linear(12*12*64, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 12*12*64)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

net = Net()

  1. Training and Evaluation

  • Training Loop: Use Cross-Entropy Loss and Adam optimizer.
import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# Training loop
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print(f'[Epoch {epoch + 1}, Batch {i + 1}] loss: {running_loss / 100:.3f}')
            running_loss = 0.0

print('Finished Training')
  • Validation and Testing: Evaluate the model on the test set.
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy of the network on the 10000 test images: {100 * correct / total:.2f}%')

  1. Deployment

  • Deployment Strategy: Deploy the model as a web service using Flask.
  • Tools and Platforms: Use Flask for the web service and deploy on Heroku.
from flask import Flask, request, jsonify
import torch

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json(force=True)
    image = torch.tensor(data['image']).float().unsqueeze(0).unsqueeze(0)
    output = net(image)
    _, predicted = torch.max(output.data, 1)
    return jsonify({'prediction': predicted.item()})

if __name__ == '__main__':
    app.run(debug=True)

Conclusion

Congratulations! You have successfully completed the custom project module. By now, you should have a solid understanding of how to apply PyTorch to solve real-world problems. This project has allowed you to practice data preprocessing, model building, training, evaluation, and deployment. Keep experimenting with different datasets and models to further enhance your skills. Happy coding!

◄ Previous
◄
End!
The Project | About Us | Contribute | Donations | License
© Copyright 2024. All rights reserved