In the realm of deep learning and neural networks, certain models have made their mark as significant contributors to the field. One such model is VGGNet, an architecture that revolutionized the way we perceive the depth of networks.
Imagine standing on the shoulders of a giant, where you can see beyond the horizon. That’s exactly the kind of leap VGGNet brought to the world of convolutional neural networks (CNNs). But before we delve into the intricacies of VGGNet, let’s take a step back and understand the foundation on which it stands.
The Genesis of Convolutional Neural Networks
As we’ve previously discussed in the article, “Convolutions: The Magic Behind Neural Networks,” the core of CNNs lies in their capability to process images. They do this through a mathematical operation known as convolution, which enables the extraction of features from input images.
To make a long story short, CNNs are the go-to neural network architecture when it comes to image processing.
But, the concept of CNNs is not a single-layered structure; it’s a multi-tiered skyscraper of architectural components, each playing a unique role in the processing and understanding of image data. One of the foundational layers of this skyscraper is the Multi-Layer Perceptron (MLP), a ‘silent workhorse’ of neural networks, as we’ve detailed in “Demystifying Multi-Layer Perceptron: The Unsung Hero of Neural Networks.”
The Evolution of Neural Networks: Enter VGGNet
Following the heritage of AlexNet, detailed in “AlexNet: The Breakthrough in Deep Learning,” came a new player on the field: VGGNet. It didn’t just enter the scene; it made a grand entrance, setting a new standard for network depth and complexity.
VGGNet, also known as Visual Geometry Group Network, was introduced by the Visual Geometry Group at Oxford. It played a pivotal role in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014. Its design principles centered around the belief that increasing depth through smaller filters could enhance the model’s performance. And boy, were they right!
It’s imperative to note that VGGNet’s depth contributed to its performance, but it wasn’t the sole reason for its success. The neural network’s depth worked in tandem with various other factors such as avoiding overfitting, maintaining a balance in bias-variance, and implementing dropout. These concepts have been elaborately discussed in our articles, “Tackling Bias and Variance: Perfecting the Balance in Neural Networks,” “The Art of Regularization: Taming Overfitting,” and “Dropout: A Key to Demystifying Machine Learning,” respectively.
Diving into the Depths: Unpacking VGGNet Architecture
VGGNet’s architecture is both sophisticated and surprisingly straightforward. It consists of multiple convolutional layers followed by max pooling and fully connected layers. What makes VGGNet stand out from its predecessors is its uniformity – all convolutional layers use small 3×3 receptive fields (the local region of the input that the layer is connected to). The use of smaller, more manageable receptive fields was a strategic move, giving VGGNet the advantage of covering the same receptive field as larger-sized filters while keeping fewer parameters and enjoying the benefits of more non-linearities.
A Symphony of Layers
There are several variants of VGGNet, but the most commonly used are VGG-16 and VGG-19, named for the number of weight layers in the network (13 convolutional layers and 3 or 6 fully connected layers, respectively). Here’s a simplified representation of VGG-16:
- Input Layer: An input image of fixed size 224 x 224 RGB.
- Convolutional Layers: The main building block of VGGNet, each using a 3×3 receptive field, stride 1, and ‘same’ padding to preserve spatial resolution.
- ReLU Activation: The Rectified Linear Unit (ReLU) activation function is applied after each convolution.
- Pooling Layers: Max pooling is performed over a 2×2 pixel window with stride 2 to reduce the spatial dimensions.
- Fully Connected Layers: Three fully connected layers follow the final pooling layer, with the first two having 4096 channels each.
- Softmax Layer: The final layer is a softmax classification layer.
- Dropout: To reduce overfitting, dropout regularization is applied to the first two fully connected layers.
This combination of layers works in harmony, each playing its part in creating a neural network capable of producing outstanding results on image classification tasks.
The Good and the Not-So-Good
Just as we can’t ignore the remarkable capabilities of VGGNet, we also can’t overlook some of its less favorable qualities. Yes, VGGNet set new standards for network depth, but it also increased the computational cost. The use of small filter sizes led to more parameters, and thus a greater need for computational power and memory.
VGGNet, while offering improved accuracy, is also a memory guzzler and significantly slower compared to its counterparts.
Moreover, despite employing dropout and other regularization techniques (discussed in our article, “Beware of Overfitting: A Subtle Saboteur“), VGGNet can still fall prey to overfitting due to its depth.
Comparing VGGNet with Other Models
When discussing deep learning architectures, it’s essential to understand the broader landscape and see how different models compare. Let’s take a moment to place VGGNet alongside its contemporary, AlexNet, and its successor, ResNet.
VGGNet vs AlexNet
AlexNet, as discussed in “AlexNet: The Breakthrough in Deep Learning,” was the trailblazer that put CNNs on the map. However, VGGNet took the principles of AlexNet and extended them, primarily by dramatically increasing the network’s depth.
While AlexNet had only five convolutional layers, VGGNet (in its most common forms, VGG-16 and VGG-19) has 13 to 16 convolutional layers. This increased depth allowed VGGNet to learn more complex features. However, it also made the network more computationally expensive and prone to overfitting compared to AlexNet.
VGGNet vs ResNet
ResNet, or Residual Network, is a more recent deep learning architecture that built upon the lessons learned from VGGNet. Like VGGNet, ResNet uses a deep network with small 3×3 convolutions. However, ResNet introduced a new component: skip connections or shortcut connections, which allow the network to bypass layers during training.
These skip connections alleviate the vanishing gradient problem, a challenge that arises when training very deep neural networks like VGGNet. They allow ResNet to train networks with a depth of over 100 layers effectively—far beyond what VGGNet could handle.
Building VGGNet with PyTorch from Scratch
Here I will present the code in full and then break it down with piece by piece explanations:
import torch from torch import nn class VGGNet(nn.Module): def __init__(self): super(VGGNet, self).__init__() self.features = nn.Sequential( # Conv Layer block 1 nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # Conv Layer block 2 nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # Conv Layer block 3 nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # Conv Layer block 4 nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), # Conv Layer block 5 nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), ) self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 1000), ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x # Create the VGGNet model vggnet_model = VGGNet() print(vggnet_model)
The remarkable depth of VGGNet can seem intimidating at first glance, but fear not! With the power of PyTorch, a popular deep learning framework, we can construct VGGNet with relative ease. Let’s break it down.
import torch from torch import nn
We begin by importing the necessary libraries:
torch for the PyTorch framework itself, and
nn for PyTorch’s neural network module. This module contains the building blocks we need to construct VGGNet.
class VGGNet(nn.Module): def __init__(self): super(VGGNet, self).__init__()
We define our VGGNet as a class that inherits from
nn.Module, the base class for all neural network modules in PyTorch. The
super function is called to allow us to use the methods from the parent class.
__init__ method, we define the architecture of our network. VGGNet is composed of a series of convolutional layers followed by fully connected layers. We split these into two main parts:
self.features for the convolutional layers, and
self.classifier for the fully connected layers.
self.features = nn.Sequential(... self.classifier = nn.Sequential(...
nn.Sequential is a container that allows us to stack different types of layers in sequence. Inside
self.features, we define multiple blocks of Convolutional layers and Max Pooling layers. After each Convolutional layer, a Rectified Linear Unit (ReLU) activation function is applied. The Max Pooling layer is used to reduce the spatial dimensions of the output volumes.
self.classifier includes three fully connected layers that follow the final pooling layer, the first two of which are followed by ReLU activations and Dropout operations for regularisation.
def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x
forward method, we define the forward pass of our network. We first apply the convolutional and pooling layers defined in
self.features. Afterward, we reshape the output tensor so it can be input to the fully connected layers defined in
Finally, we create an instance of the VGGNet model and print it to see the architecture.
vggnet_model = VGGNet() print(vggnet_model)
And there we have it! We’ve constructed a simplified version of VGGNet using PyTorch. This script provides a blueprint that you can extend and modify to meet your specific needs, whether you’re tackling an image classification problem or delving into more advanced deep learning tasks.
The Impact and Legacy of VGGNet
VGGNet, despite its computational intensity and tendency towards overfitting, has carved a unique niche for itself in the annals of deep learning. Its contribution goes beyond just accuracy in image recognition tasks—it also introduced valuable design principles that continue to guide the development of newer architectures.
The Echoes of VGGNet
While VGGNet may no longer be the first choice for image classification tasks due to more efficient models, its architecture’s influence resonates in many deep learning applications. It’s widely used as a feature extractor in transfer learning—a technique that leverages pre-trained models to gain insights from data that the model wasn’t originally trained on. Its uniform architecture makes it a particularly good choice for this application.
Moreover, VGGNet’s principles of increasing depth with small filters have become a common practice in designing newer architectures. It has paved the way for subsequent breakthroughs in the field, including models like ResNet, which capitalized on VGGNet’s depth principles while addressing its shortcomings.
The Road Ahead
“The story of VGGNet is an essential chapter in the book of deep learning, reminding us that innovation often requires challenging the status quo.”
VGGNet’s legacy lies not just in its performance or its architecture, but also in the mindset it represents. It’s a testament to the spirit of exploration and boundary-pushing that drives the field of machine learning forward.
As we continue to delve into the world of neural networks and deep learning, let’s remember the lessons from VGGNet: the value of depth, the power of simplicity, and the importance of continuing to push the boundaries of what’s possible.
VGGNet is a landmark in the journey of deep learning, a journey that we at RabbitML are excited to guide you through. As we continue to demystify machine learning, we hope you’ll join us for the ride!
And that, dear readers, concludes our journey into the depths of VGGNet. Stay tuned for more deep dives into the fascinating world of deep learning and machine learning here at RabbitML. Until next time!