CIFAR-10 tutorial with Keras

Notice

** I have published the contents of the tutorial in this article so far, but since it will be large and it will be troublesome to update twice, please refer to the note on Github for details. I think that would be better because I think I can follow the latest content. In addition, we will continue to inform you about updates on Github on this page. ** **

The repository is here If you want to see only the contents of the tutorial, please click here If you find any errors in the tutorial content, please let us know in the comments on the article or on the issue page on Github.

--CIFAR-10 Tutorial: Contents of this article --CIFAR-10 Tutorial [Appendix]: A little tip --CIFAR-10 Tutorial [Transfer Learning] : About transfer learning using CIFAR-10. I might write an article someday. --How to use the dlt package /: A brief explanation of how to use the dlipr package using Fashion-MNIST --results /: Save the output ofCIFAR-10 Tutorial --ʻImages / : Save example images and other images ---tltag:CIFAR-10 tutorial [transfer learning]` results obtained

Change log

--2018/02/03: The tutorial has been arranged so that it can be viewed as a website without cloning from Github.

CIFAR-10 tutorial

1.First of all

This tutorial deals with an image classification problem called CIFAR-10. The CIFAR-10 consists of 50,000 32x32 pixel color image data and 10 labels that classify it: airplanes, cars, birds, cats, deer, dogs, frogs, horses, boats, trucks. .. There is also a CIFAR-100, which is classified into more subdivided labels, and this one has 100 labels. Both are basically the same, so this time I will use CIFAR-10.

Also, use Keras for implementation. Keras cannot be used for unsupervised learning, but I think Keras is sufficient for dealing with typical machine learning problems that represent CIFAR-10 and MNIST. What's more, Keras is very easy to implement for simple networks (not just one-way networks, but branching things, RNNs, etc.), so even beginners (rather than raw Tensorflow) I think it's easy to understand what you're doing. This tutorial also explains how to implement Keras (although it's easy), so even if you don't know Keras, you can read it. If you get stuck, please refer to the Official Document.

This tutorial is based on what I learned in a university lecture, so I added extra explanations where it took me a long time to understand. I can't deny the overall redundant feeling, but I think that leaving it on purpose will be useful for people with similar problems.

Features of this tutorial

--What not to do ――What is machine learning? --About Tensorflow --Improve accuracy (hyperparameter adjustment method, etc.)

Recommended Posts

CIFAR-10 tutorial with Keras
Image recognition with keras
Exposing the DCGAN model for Cifar 10 with keras
Multivariate LSTM with Keras
Learn librosa with a tutorial 1
Install Keras (used with Anaconda)
Multiple regression analysis with Keras
Auto Encodder notes with Keras
Implemented word2vec with Theano + Keras
Tuning Keras parameters with Keras Tuner
Get started with Django! ~ Tutorial ④ ~
Get started with Django! ~ Tutorial ⑥ ~
Easily build CNN with Keras
Implemented Efficient GAN with keras
[Keras] 75% accuracy on CIFAR10 dataset
Image recognition with Keras + OpenCV
MNIST (DCNN) with Keras (TensorFlow backend)
[PyTorch Tutorial ⑤] Learning PyTorch with Examples (Part 2)
Predict Kaggle's Titanic with keras (kaggle ⑦)
[TensorFlow] [Keras] Neural network construction with Keras
Implement Keras LSTM feedforward with numpy
Compare DCGAN and pix2pix with keras
Score-CAM implementation with keras. Comparison with Grad-CAM
Prediction of sine wave with keras
Beginner RNN (LSTM) | Try with Keras
[PyTorch Tutorial ⑤] Learning PyTorch with Examples (Part 1)
Write Reversi AI with Keras + DQN
Doing Blender's Add-on Tutorial with Hy
4/22 prediction of sine wave with keras
Parameter optimization automation with Keras with GridSearch CV
[In-Database Python Analysis Tutorial with SQL Server 2017]
Try implementing XOR with Keras Functional API
Compare raw TensorFlow with tf.contrib.learn and Keras
Attempt to classify emoji fonts with Keras
Run Keras with CNTK backend from CentOS
Face recognition of anime characters with Keras
Tuning hyperparameters with GridSearch using pipeline with keras
Classified ImageNet burgers and bicycles with Keras
Use TPU and Keras with Google Colaboratory