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An overview of VGG16 and NiN models

This post aims to introduce briefly two classic convolutional neural networks, VGG16 and NiN (a.k.a Network in Network). We are going to discover their architectures as well as their implementations on the Keras platform. You can refer to my previous blogs for some related topics: Convolutional neural networks, LeNet, and Alexnet models.

I. VGG model

1. Introduction

VGG is a deep convolutional neural network that was proposed by Karen Simonyan and Andrew Zisserman [1]. VGG is an acronym for their group name, Visual Geometry Group, from the Oxford University. This model secured 2nd place in the ILSVRC-2014 competition where 92.7% classification performance was achieved. The VGG model investigates the depth of layers with a very small convolutional filter size (3 × 3) to deal with large-scale images. The authors released a series of VGG models with different layer lengths, from 11 to 19, which is presented in the following table.

Table 1: Different configurations of VGG. Source

In summary:

  • All configurations of VGG have block structures.
  • Each VGG block consists of a sequence of convolutional layers which are followed by a max-pooling layer. The same kernel size (3 × 3) is applied over all convolutional layers. Besides, the authors used a padding size of 1 to keep the size of the output after each convolutional layer. A max-pooling of size 2 × 2 with strides of 2 is also applied to halve the resolution after each block
  • Each VGG model has two fully connected hidden layers and one fully connected output layer.

In this post, we only focus on the deployment of the VGG16, its architecture as well as its implementation on Keras. Other configurations are constructed similarly.

The structure of VGG16 is described by the following figure:

Figure 1: The architecture of VGG16. Source: Researchgate.net

VGG16 is composed of 13 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. Therefore, the number of layers having tunable parameters is 16 (13 convolutional layers and 3 fully connected layers). That is the reason why the model name is VGG16. The number of filters in the first block is 64, then this number is doubled in the later blocks until it reaches 512. This model is finished by two fully connected hidden layers and one output layer. The two fully connected layers have the same neuron numbers which are 4096. The output layer consists of 1000 neurons corresponding to the number of categories of the Imagenet dataset. In the next section, we are going to implement this architecture on Keras.

2. Implementation of VGG16 on Keras

Firstly, we need to import some necessary libraries:

Once all necessary libraries are ready, the model can be implemented by the following function:

Now, let’s see the detailed information in each layer of the model:

vgg16_model = VGG16()vgg16_model.summary()Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
conv2d_1 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
conv2d_3 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 56, 56, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
conv2d_5 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
conv2d_6 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 28, 28, 256) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
conv2d_8 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 14, 14, 512) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
dense (Dense) (None, 4096) 102764544
_________________________________________________________________
dropout (Dropout) (None, 4096) 0
_________________________________________________________________
dense_1 (Dense) (None, 4096) 16781312
_________________________________________________________________
dropout_1 (Dropout) (None, 4096) 0
_________________________________________________________________
dense_2 (Dense) (None, 1000) 4097000
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________

As the number of filters increases following the model depth, hence the number of parameters increases significantly in the later layers. Especially, the parameter number in the two fully connected hidden layers is very large, with 102, 764, 544, and 16, 781, 312 parameters, respectively. It accounts for 86.4% parameters of the whole model.

A large number of parameters may reduce the model performance. Sometimes, it leads to overfitting. A natural question arises: Is it possible to replace the fully connected layers with something to reduce the model complexity? The NiN model, which we are going to discuss in the next section, is an appropriate answer to this question.

II. NiN model

1. Introduction

Network in Network (NiN) is a deep convolutional neural network introduced by Min Lin, Qiang Chen, Shuicheng Fan [2]. The structure of this network is different from the classic CNN models:

  • The classic models use linear convolutional layers and the layers are followed by an activation function to scan the input, while the NiN uses multilayer perceptron convolutional layers, at which each layer includes a micro-network.
Figure 2: Comparison of Linear convolutional layer and Multilayer perception convolutional layer (Mlpconv layer). Source
  • The classic models apply fully connected layers at the end of the model to classify objects, while the NiN uses a global average pooling layer before feeding the output to the softmax layer. The global average pooling layer has some advantages compared to the traditional fully connected layers. Firstly, it is more native to the convolution structure by enforcing correspondences between feature maps and categories. Secondly, there is no parameter to optimize in the global average pooling layer, so it helps to avoid the overfitting phenomena. Finally, using the global average pooling layer is more robust to spatial translations of the input, because it sums out the spatial information.
Figure 3: The overall structure of NiN: three MLP convolutional layers and one global average pooling layer. Source

2. Implementation of NiN on Keras

The original NiN network is composed of four NiN blocks. Each block includes three convolutional layers:

  • The first layer uses a filter window whose shape belongs to {11 × 11, 5 × 5, 3 × 3}.
  • The last two layers are 1 × 1 convolutional layers.

Each NiN block is followed by a Max-pooling layer with pooling size 3 × 3, and strides of 2. Except the last block is followed by a Global Average Pooling layer.

Figure 4: The architecture of the NiN model. Source

This model is implemented easily by the following function:

Let’s consider the detailed information (i.e the output size and parameter number) for each layer of the model:

NiN_model = NiN()
NiN_model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_13 (Conv2D) (None, 54, 54, 96) 34944
_________________________________________________________________
conv2d_14 (Conv2D) (None, 54, 54, 96) 9312
_________________________________________________________________
conv2d_15 (Conv2D) (None, 54, 54, 96) 9312
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 26, 26, 96) 0
_________________________________________________________________
conv2d_16 (Conv2D) (None, 22, 22, 256) 614656
_________________________________________________________________
conv2d_17 (Conv2D) (None, 22, 22, 256) 65792
_________________________________________________________________
conv2d_18 (Conv2D) (None, 22, 22, 256) 65792
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 10, 10, 256) 0
_________________________________________________________________
conv2d_19 (Conv2D) (None, 8, 8, 384) 885120
_________________________________________________________________
conv2d_20 (Conv2D) (None, 8, 8, 384) 147840
_________________________________________________________________
conv2d_21 (Conv2D) (None, 8, 8, 384) 147840
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 3, 3, 384) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 3, 3, 384) 0
_________________________________________________________________
conv2d_22 (Conv2D) (None, 1, 1, 10) 34570
_________________________________________________________________
conv2d_23 (Conv2D) (None, 1, 1, 10) 110
_________________________________________________________________
conv2d_24 (Conv2D) (None, 1, 1, 10) 110
_________________________________________________________________
global_average_pooling2d (Gl (None, 10) 0
_________________________________________________________________
dense_3 (Dense) (None, 1000) 11000
=================================================================
Total params: 2,026,398
Trainable params: 2,026,398
Non-trainable params: 0
_________________________________________________________________

Remark that the global average pooling layer has no parameter. Hence using this layer instead of fully connected layers helps to reduce significantly the model complexity. The parameter number of this model is much smaller compared to the one of the VGG model.

Conclusion: We have discovered the architectures of VGG and NiN models. The construction of the VGG model is similar to the previous models, LeNet and Alexnet. They are all composed of convolutional layers, pooling layers, and terminated by fully connected layers. The reasonable depth extension of VGG makes it outperform the previous ones. However, the number of parameters in fully connected layers is too large, especially in cased of large-scale image processing. NiN overcomes this drawback by replacing these layers with a global average pooling layer. This layer has some advantages, it is more native to enforce the correspondences between feature maps and categories. Besides, there is no parameter to optimize in this layer. Hence, using this layer helps to avoid overfitting while training the model. The appearance of the NiN model is also an inspiration for the construction of later modern CNN models that we are going to discuss in the ne xt posts.

I hope that this post is helpful for you! Don’t hesitate to follow my medium blog to receive related topics.

Thanks for reading!

Github code: https://github.com/KhuyenLE-maths/An_overview_of_VGG_and_NiN_models/blob/main/An_overview_of_VGG16_and_NiN_models.ipynb

My blog page: https://lekhuyen.medium.com/

______________________________________________________

References:

[1] Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).

[2] Lin, Min, Qiang Chen, and Shuicheng Yan. “Network in network.” arXiv preprint arXiv:1312.4400 (2013).

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