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Several simple deep learning exercises by different ANNs (CNN, RNN, GAN)

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NNs

Deep Learning Exercises

Overview

Training and test dataset

  • CIFAR-10: The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
  • ImageNet10: A subset of images from the ImageNet dataset, which contains 9000 images belonging to ten object classes.
  • Flickr8k: This repository contains a copy of machine learning datasets used in tutorials on MachineLearningMastery.com.
  • Iris: This is perhaps the best known database to be found in the pattern recognition literature. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.

Network models

Building and Runnning

For most .ipynb files, you can directly open it in Colab to run.
As for Image Caption Generation.ipynb, Flickr8k dataset is very large, so if you want to work on Colab, it is recommended to download the zip files, unzip them, and then upload all the files to your Google Drive. This initial upload may take a while, but from then on you will only need to mount your Drive every time you start a new Colab session and the data will be immediately accessible. Mounting only takes a few second. Do not forget to replace the path with your own root data directory of your Google Drive.
As for .py files in the Perceptron folder, especially the perceptron.py. You have to open terminal to type: python perceptron.py <class> with/without, the class can be setosa, versicolor and verginica. And with means with learning rate 0.01, without means without using learning rate.

License

知识共享许可协议

This repository is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

本作品采用知识共享署名 4.0 国际许可协议进行许可。

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Several simple deep learning exercises by different ANNs (CNN, RNN, GAN)

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