Understanding convolutional layer
Source code is uploaded on github.The sample image is obtained from PEXELS. What is the difference between convolutional layer and linear layer? What ...
Category: Chainer
Basic image processing tutorial
Basic image processing for deep learning. Refer github for the source code.The sample image is obtained from PEXELS. If you are not familiar with...
MNIST inference code
We already learned how to write training code in chainer, the last task is to use this trained model to inference (predict) the test input MNIST image. Inferenc...
Chainer family
[Updated on May 31: Add detail description for ChainerCV & ChainerMN] Recently several sub-libraries for Chainer are released, ChainerRL RL: Reinforcement L...
Writing organized, reusable, clean training code using Trainer module
Training code abstraction with Trainer Until now, I was implementing the training code in “primitive” way to explain what kind of operations are going on in dee...
Design patterns for defining model
[Update 2017.06.11] Add Chainer v2 code Machine learning consists of training phase and predict/inference phase, and what model need to calculate is diffe...
Refactoring MNIST training
Previous section, we learned minimum implementation (train_mnist_1_minimum.py) for the training code for MNIST. Now, let’s refactor the codes. See train_mn...
MNIST training with Multi Layer Perceptron
[Update 2017.06.11] Add chainer v2 code. Training MNIST You already studied basics of Chainer and MNIST dataset. Now we can proceed to the MNIST classific...
MNIST dataset introduction
MNIST dataset MNIST (Mixed National Institute of Standards and Technology) database is dataset for handwritten digits, distributed by Yann Lecun’s THE...
Why Chainer?
I will list up good points of Chainer as an opinion from one Chainer enthusiast. Features Easy environment setup Environment setup is easy, execute one command&...