Setting up a multilabel classification network with torch-dataframe

Working with multiple outcomes per input can be challenging. The image is cc by  Markus Lütkemeyer.

Working with multiple outcomes per input can be challenging. The image is cc by Markus Lütkemeyer.

A common situation is that you have an image that can represent more than one class, e.g. a image may both have an oil tanker and an oil platform. You also may have missing data for some of these that you don’t want to evaluate. In my research this problem occurs and my solution so far has been a my own [criterion_ignore](https://github.com/gforge/criterion_ignore) that sets the errors for ignore labels to zero. This post will be a quick look at how to combine the torch-dataframe with the criterion_ignore. Continue reading

Integration between torchnet and torch-dataframe – a closer look at the mnist example

It's all about the numbers and getting the tensors right. The image is cc by David Asch .

It’s all about the numbers and getting the tensors right. The image is cc by David Asch
.

In previous posts we’ve looked into the basic structure of the torch-dataframe package. In this post we’ll go through the [mnist example][mnist ex] that shows how to best integrate the dataframe with [torchnet](https://github.com/torchnet/torchnet). Continue reading

The torch-dataframe – subsetting and sampling

Subsetting and batching is like dealing cards - should be random unless you are doing a trick. The image is cc from Steven Depolo.

Subsetting and batching is like dealing cards – should be random unless you are doing a trick. The image is cc from Steven Depolo.

In my previous two posts I covered the most basic data manipulation that you may need. In this post I’ll try to give a quick introduction to some of the sampling methods that we can use in our machine learning projects. Continue reading

The torch-dataframe – basics on modifications

Forming your data to your needs is crucial. The image i cc by Lennart Tange.

Forming your data to your needs is crucial. The image i cc by Lennart Tange.

In my [previous post][intro post] we took a look at some of the basic functionality. In this post I’ll try to show how to manipulate your dataframe. Note though, the [torch-dataframe][tdf github] is not about data munging, there are far more powerful tools in other languages for this. The aim of the modifications is to do simple tasks without being forced to switch to a different language. Continue reading

Deep learning with torch-dataframe – a gentle introduction to Torch

[![A solid concrete foundation is always important. The image is cc by Sharon Pazner ](http://gforge.se/wp-content/uploads/2016/07/Lego-house-concrete.jpg)](http://gforge.se/wp-content/uploads/2016/07/Lego-house-concrete.jpg) A solid concrete foundation is always important. The image is cc by[
Sharon Pazner
](https://flic.kr/p/nSNQzw)

Handling [tabular data](https://en.wikipedia.org/wiki/Table_(information)) is generally at the heart of most research projects. As I started exploring [Torch](http://torch.ch/) that uses the [Lua](https://www.lua.org/) language for [deep learning](https://en.wikipedia.org/wiki/Deep_learning) I was surprised that there was no package that would correspond to the functionality available in R’s [data.frame](https://stat.ethz.ch/R-manual/R-devel/library/base/html/data.frame.html). After some searching I found Alex Mili’s [torch-dataframe](https://github.com/AlexMili/torch-dataframe) package that I decided to update to my needs. We have during the past few months been developing the package and it has now made it onto the Torch [cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet#data-formats) (partly the reason for the posting scarcity lately). This series of posts provide a short introduction to the package (version 1.5) and examples of how to implement basic networks in Torch. Continue reading