CIFAR-10 is a popular benchmark dataset in the field of machine learning, especially for image classification tasks. In this article, we will explore how to implement various Convolutional Neural Network (CNN) architectures using Keras and TensorFlow. If you’re ready to dive in, let’s get started!

Requirements

Before we jump into the implementation steps, make sure you have the following installed:

• Python (3.5)
• Keras (= 2.1.5)
• TensorFlow GPU (= 1.4.1)

Architecture Overview

We’ll be working with several notable CNN architectures, each inspired by different research papers:

• LeNet – [LeNet-5 – Yann LeCun](http://yann.lecun.com/exdb/lenet)
• Network in Network – [Network In Network](https://arxiv.org/abs/1312.4400)
• VGG19 Network – [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
• Residual Network – [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
• Wide Residual Network – [Wide Residual Networks](https://arxiv.org/abs/1605.07146)
• DenseNet – [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
• SENet – [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)

Accuracy of Implementations

Here’s a comparison of CNNs trained on the CIFAR-10 dataset and their accuracy:

 network                GPU        params   batch size  epoch  training time  accuracy(%) 
:----------------------:---------::-------::----------::-----::-------------::-----------:
Lecun-Network          GTX1080TI  62k        128        200      30 min         76.23     
Network-in-Network     GTX1080TI  0.97M      128        200      1 h 40 min     91.63     
Vgg19-Network          GTX1080TI  39M        128        200      1 h 53 min     93.53     
Residual-Network20     GTX1080TI  0.27M      128        200      44 min         91.82     
Residual-Network32     GTX1080TI  0.47M      128        200      1 h 7 min      92.68     
Residual-Network110    GTX1080TI  1.7M       128        200      3 h 38 min     93.93     
Wide-resnet 16x8       GTX1080TI  11.3M      128        200     4 h 55 min      95.13     
Wide-resnet 28x10      GTX1080TI  36.5M      128        200     10 h 22 min     95.78     
DenseNet-100x12        GTX1080TI  0.85M      64         250     17 h 20 min     94.91     
DenseNet-100x24        GTX1080TI  3.3M       64         250     22 h 27 min     95.30     
DenseNet-160x24        1080 x 2   7.5M       64         250     50 h 20 min     95.90     
ResNeXt-4x64d          GTX1080TI  20M        120        250     21 h 3 min      95.19     
SENet(ResNeXt-4x64d)   GTX1080TI  20M        120        250     21 h 57 min     95.60  

This data shows not only the accuracy achieved by each model, but also the computing resources and training times involved.

Understanding the Models – An Analogy

Think of building a CNN similar to constructing a multilayered cake. Each layer (or convolutional layer) contributes its own unique flavor (features) to the overall cake (the final classification result). Just as a baker must carefully choose ingredients and layers to create the perfect cake, a data scientist must select the right architecture and hyperparameters that optimize performance for a specific dataset like CIFAR-10.

Training Tips and Tricks

The first CNN network by Yann LeCun, LeNet, serves as an excellent baseline. Here are some essential training tricks:

 network                  GPU       DP           DA        WD   training time  accuracy(%) 
:-----------------------:---------::-------::----------::-----::-------------::-----------:
LeNet_keras             GTX1080TI    -        -           -       5 min          58.48     
LeNet_dp_keras          GTX1080TI    √        -           -       5 min          60.41     
LeNet_dp_da_keras       GTX1080TI    √        √           -       26 min         75.06     
LeNet_dp_da_wd_keras    GTX1080TI    √        √           √       26 min         76.23  

Through data preprocessing (DP), data augmentation (DA), and weight decay (WD), you can significantly improve accuracy.

Troubleshooting

If you encounter issues, consider these troubleshooting tips:

• Adjust the batch size based on your GPU’s memory capacity; increasing or decreasing it may yield different results.
• Experiment with different learning rate schedules to optimize training accuracy.
• If your model’s accuracy isn’t improving, review your data preprocessing and augmentation techniques.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.