Kuzushiji-MNIST exploring

Overview

Kuzushiji-MNIST is MNIST like data set based on classical Japanese letters.
The following image is part of the data set. As you can see, this is composed of visually complex letters.



On this article, I’ll do simple introduction of Kuzushiji-MNIST and classification with Keras model.

Slack bot for image stylization by magenta

Overview

With magenta and Slack’s Bots application, I made an image style change bot.
This works as following gif.



When we post the image to the bot, it adds the style to the image and posts the images to the slack channel.

tf.estimator of TensorFlow lets us concisely write deep neural network

Overview

On this article, I’ll re-write the simple deep neural network model to iris data by tf.estimator. From official page,

TensorFlow’s high-level machine learning API (tf.estimator) makes it easy to configure, train, and evaluate a variety of machine learning models.

By comparing with the original code, I’ll check how much it becomes concise and how to use tf.estimator.

How to make Fine tuning model by Keras

Overview

Fine-tuning is one of the important methods to make big-scale model with a small amount of data.

Usually, deep learning model needs a massive amount of data for training. But it is not always easy to get enough amount of data for that. To be added, in many cases, it takes much time to make model from the viewpoint of training. I know you don’t like to see one epoch of training using the time from sunrise to sunset. In some areas like image classification, you can use fine-tune method to solve this situation.

For example, when you try to make image classification model, very deep CNN model works well(sometimes and other time not). To make that kind of model, it is necessary to prepare a huge amount of data. However, by using the model trained by other data, it is enough to add one or some layers to that model and train those. It saves much time and data.

Here, I show this type of method, fine-tuning, by Keras.

How Dropout works on Neural Network

Overview

Dropout is one of the good techniques to make good neural network model. The system of it is very simple.

Practical hack to make deep learning model

Overview

Neural network has a lot of flexibility in its design. You can choose and set many components and options. Because of that, to make more optimized network, you need to know and care about the procedures to adjust those to update your network efficiently.
Here, I arranged neural network’s components and in which procedure those should be adjusted.

Simple guide to Neural Network

What is Neural network?

Neural network is an algorithm which make input go through at least one hidden and output layers to output.
Graphically it is like below.

CNN + KNN model accuracy

Overview

On the contest site like Kaggle, we can see many trials and good scores by the combination of some methods.
For example, you can get scores by logistic regression and lasso regression. You can make xgboost model by using those scores.
This time, about cifar-10, I make CNN model. And by using the score, I check KNN scores.

Breakout by tensorflow model

Overview

I made a Tensorflow model of breakout by the data which is from my playing.
The purpose of this is to visually observe how outcome of the prediction works. So this time ‘theoretical accuracy’ should be left behind.
I just made simple and easy model without thinking about details and tried to make the model play breakout like the following image.

The one I used as breakout is from address.
breakout