Explore different VGG networks. What do you discover?

1 Question

Explore different VGG networks.

2 Methods

The VGG network is a classic convolutional neural network structure. Its main feature is the use of very small convolution kernels and pooling layers. By continuously stacking these small convolution kernels and pooling layers, a 16-19-layer deep convolutional neural network is successfully constructed. In addition to VGG-16 and VGG-19, there are also different versions of VGG networks such as VGG-11 and VGG-13. The main difference between these networks is that their depth and number of parameters are different, so their performance is also different.

 import torch import torch.nn as nn class VGG(nn.Module): def __init__(self, depth, num_classes): super(VGG, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(128, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(128, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=(2, 1)), nn.Conv2d(256, 512, kernel_size=(3, 3), padding=(0, 1)), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=(3, 3), padding=(0, 1)), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=(2, 1)), nn.Conv2d(512, 512, kernel_size=(3, 3), padding=(0, 1)), nn.ReLU(inplace=True), nn.Conv2d(512, 512, kernel_size=(3, 3), padding=(0, 1)), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=(2, 1)), ) self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() self.depth = depth

3 Conclusion

To explore different VGG networks, the code defines a VGG network model where the depth parameter controls the depth of the convolutional layer. In each convolutional block, we use the same number of convolutional layers to keep the feature map size constant and continuously increase the number of channels. Finally, we add two fully connected layers to output the final classification results.

The disadvantage is that the model does not use any regularization techniques, which may cause the model to overfit the training data and reduce its generalization ability. Although the VGG network is classic, many more advanced network structures have emerged since its introduction, which can provide better performance on many tasks. Lack of more detailed hyperparameter settings. Lack of pre-processing and post-processing of input data: This may affect the training and performance of the model, especially when using images of different sizes or types.

In the future, we can study deeper network structures. Although the VGG network is relatively deep, with the improvement of hardware performance and the development of optimization technology, we can try to build deeper networks. This may lead to more complex calculations and more parameters, so we need to study how to effectively train and optimize such networks. More effective feature extraction, the VGG network improves performance by increasing the depth of the convolution layer, but this also increases the complexity of calculation. In the future, we can study how to design more effective convolution kernels, or use more advanced feature extraction methods, multimodal and multi-task learning, etc.