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PyTorch Tutorial - GeeksforGeeks

PyTorch is an open-source deep learning framework designed to simplify the process of building neural networks and machine learning models. With its dynamic computation graph, PyTorch allows developers to modify the network’s behavior in real-time, making it an excellent choice for both beginners and researchers.

To start using PyTorch, you first need to install it. You can install it via pip:

pip install torch torchvision

For GPU support (if you have a CUDA-enabled GPU), install the appropriate version:

pip install torch torchvision torchaudio cudatoolkit=11.3

A tensor is a multi-dimensional array that is the fundamental data structure used in PyTorch (and many other machine learning frameworks).

We can create tensors for performing above in several ways:

import torch

tensor_1d = torch.tensor([1, 2, 3])
print("1D Tensor (Vector):")
print(tensor_1d)
print()

tensor_2d = torch.tensor([[1, 2], [3, 4]])
print("2D Tensor (Matrix):")
print(tensor_2d)
print()

random_tensor = torch.rand(2, 3)
print("Random Tensor (2x3):")
print(random_tensor)
print()

zeros_tensor = torch.zeros(2, 3)
print("Zeros Tensor (2x3):")
print(zeros_tensor)
print()

ones_tensor = torch.ones(2, 3)
print("Ones Tensor (2x3):")
print(ones_tensor)

Output:

1D Tensor (Vector):
tensor([1, 2, 3])

2D Tensor (Matrix):

tensor([[1, 2],
[3, 4]])

Random Tensor (2x3):

tensor([[0.3357, 0.7785, 0.8603],
[0.5804, 0.9281, 0.6675]])

Zeros Tensor (2x3):

tensor([[0., 0., 0.],
[0., 0., 0.]])

Ones Tensor (2x3):

tensor([[1., 1., 1.],
[1., 1., 1.]])

PyTorch operations are essential for manipulating data efficiently, especially when preparing data for machine learning tasks.

• Indexing: Indexing lets you retrieve specific elements or smaller sections from a larger tensor.
• Slicing: Slicing allows you to take out a portion of the tensor by specifying a range of rows or columns.
• Reshaping: Reshaping changes the shape or dimensions of a tensor without changing its actual data. This means you can reorganize the tensor into a different size while keeping all the original values intact.

Let's understand these operations with help of simple implementation:

import torch

tensor = torch.tensor([[1, 2], [3, 4], [5, 6]])

element = tensor[1, 0]
print(f"Indexed Element (Row 1, Column 0): {element}")  
slice_tensor = tensor[:2, :]
print(f"Sliced Tensor (First two rows): \n{slice_tensor}")

reshaped_tensor = tensor.view(2, 3)
print(f"Reshaped Tensor (2x3): \n{reshaped_tensor}")

Output:

Indexed Element (Row 1, Column 0): 3

Sliced Tensor (First two rows):

tensor([[1, 2],
[3, 4]])

Reshaped Tensor (2x3):

tensor([[1, 2, 3],
[4, 5, 6]])

PyTorch offers a variety of common tensor functions that simplify complex operations.

• Broadcasting allows for automatic expansion of dimensions to facilitate arithmetic operations on tensors of different shapes.
• Matrix multiplication enables efficient computations essential for neural network operations.

import torch

tensor_a = torch.tensor([[1, 2, 3], [4, 5, 6]])

tensor_b = torch.tensor([[10, 20, 30]]) 

broadcasted_result = tensor_a + tensor_b 
print(f"Broadcasted Addition Result: \n{broadcasted_result}")

matrix_multiplication_result = torch.matmul(tensor_a, tensor_a.T)
print(f"Matrix Multiplication Result (tensor_a * tensor_a^T): \n{matrix_multiplication_result}")

Output:

Broadcasted Addition Result:
tensor([[11, 22, 33],
[14, 25, 36]])

Matrix Multiplication Result (tensor_a * tensor_a^T):

tensor([[14, 32],
[32, 77]])

PyTorch facilitates GPU acceleration, enabling much faster computations, which is especially important in deep learning due to the extensive matrix operations involved. By transferring tensors to the GPU, you can significantly reduce training times and improve performance.

import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device: {device}')

tensor_size = (10000, 10000)  
a = torch.randn(tensor_size, device=device)  
b = torch.randn(tensor_size, device=device)  

c = a + b  

print("Result shape (moved to CPU for printing):", c.cpu().shape)

print("Current GPU memory usage:")
print(f"Allocated: {torch.cuda.memory_allocated(device) / (1024 ** 2):.2f} MB")
print(f"Cached: {torch.cuda.memory_reserved(device) / (1024 ** 2):.2f} MB")

Output:

Using device: cuda
Result shape (moved to CPU for printing): torch.Size([10000, 10000])
Current GPU memory usage:
Allocated: 1146.00 MB
Cached: 1148.00 MB

In this section, we'll implement a neural network using PyTorch, following these steps:

In this step, we’ll define a class that inherits from torch.nn.Module. We’ll create a simple neural network with an input layer, a hidden layer, and an output layer.

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(2, 4)  
        self.fc2 = nn.Linear(4, 1)  

    def forward(self, x):
        x = torch.relu(self.fc1(x))  
        x = self.fc2(x)               
        return x

Next, we’ll prepare our data. We will use a simple dataset that represents the XOR logic gate, consisting of binary input pairs and their corresponding XOR results.

X_train = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) 
y_train = torch.tensor([[0.0], [1.0], [1.0], [0.0]])  

Now it’s time for us to instantiate our model. We’ll also define a loss function(Mean Squared Error) and choose an optimizer(Stochastic Gradient Descent) to update the model’s weights based on the calculated loss.

# Instantiate the Model, Define Loss Function and Optimizer
model = SimpleNN()  
criterion = nn.MSELoss()  
optimizer = optim.SGD(model.parameters(), lr=0.1)  

Now we enter the training loop, where we will repeatedly pass our training data through the model to learn from it.

for epoch in range(100):  
    model.train() 

    # Forward pass
    outputs = model(X_train)
    loss = criterion(outputs, y_train)  
    
    # Backward pass and optimize
    optimizer.zero_grad()  
    loss.backward()  
    optimizer.step()  

    if (epoch + 1) % 10 == 0:  
        print(f'Epoch [{epoch + 1}/100], Loss: {loss.item():.4f}')

Finally, we need to evaluate the model’s performance on new data to assess its generalization capability.

model.eval()  
with torch.no_grad(): 
    test_data = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])
    predictions = model(test_data) 
    print(f'Predictions:\n{predictions}')

Output:

Epoch [10/100], Loss: 0.2564
Epoch [20/100], Loss: 0.2263
. . .
Epoch [90/100], Loss: 0.0829
Epoch [100/100], Loss: 0.0737

Predictions:tensor([[0.3798], [0.7462], [0.7622], [0.1318]])

Dataset and DataLoader facilitates batch processing and shuffling, ensuring smooth data iteration during training.

import torch
from torch.utils.data import Dataset, DataLoader

class MyDataset(Dataset):
    def __init__(self):
        self.data = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
        self.labels = torch.tensor([0, 1, 0])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

dataset = MyDataset()
dataloader = DataLoader(dataset, batch_size=2, shuffle=True)

for batch in dataloader:
    print("Batch Data:", batch[0])  
    print("Batch Labels:", batch[1])

Torchvision provides simple tools for applying random transformations—such as rotations, flips, and scaling—enhancing the model's ability to generalize on unseen data.

import torchvision.transforms as transforms
from PIL import Image


image = Image.open('example.jpg')  # Replace 'example.jpg' with your image file

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor()
])

augmented_image = transform(image)
print("Augmented Image Shape:", augmented_image.shape)

Batch processing improves computational efficiency and accelerates training, especially on hardware accelerators.

for epoch in range(2):  
    for inputs, labels in dataloader:
        
        outputs = inputs + 1  
        print(f"Epoch {epoch + 1}, Inputs: {inputs}, Labels: {labels}, Outputs: {outputs}")

By combining the power of Datasets, Dataloaders, data augmentation, and batch processing, PyTorch offers an effective way to handle data, streamline training, and optimize performance for machine learning tasks.

• PyTorch simplifies the implementation of CNNs using modules like torch.nn.Conv2d and pooling layers.
• Integrating batch normalization with torch.nn.BatchNorm2d helps stabilize learning and accelerate training by normalizing the output of convolutional layers.

• Implementing RNNs in PyTorch is straightforward with torch.nn.LSTM and torch.nn.GRU modules.
• RNNs, including LSTMs and GRUs, are perfect for sequential data tasks.

• PyTorch makes it easy to constructGenerative Models, including:
  • Generative Adversarial Networks (GANs): Involve a generator and a discriminator that compete to create realistic data.
  • Variational Autoencoders (VAEs): Learn probabilistic mappings, facilitating various applications in data generation.

1. Fine-Tuning Pretrained Models: PyTorch makes fine-tuning pretrained models straightforward. By using models trained on extensive datasets like ImageNet, you can easily modify the final layers and retrain them on your dataset, capitalizing on the pretrained features while tailoring the model to your specific needs.
2. Implementing Transfer Learning with torchvision.models: torchvision.models module offers a variety of pretrained models, including ResNet, VGG, and Inception. Loading a pretrained model and replacing its classifier with your custom layers is simple, ensuring the model is tailored for your dataset.
3. Freezing and Unfreezing Layers: An essential aspect of transfer learning is the ability to freeze and unfreeze layers in the pretrained model. Freezing certain layers prevents their weights from updating, preserving learned features. This technique is beneficial for focusing on training newly added layers. Conversely, unfreezing layers allows for fine-tuning, enabling the model to adjust its weights based on your dataset for improved performance.

Overall, PyTorch provides a flexible framework for transfer learning, empowering developers to efficiently adapt and optimize models for new tasks while leveraging existing knowledge.

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