# DAVIDSTUTZ

Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz

# TAG»LUA«

12thMAY2017

## Generative Adversarial Network in Torch

Simple example of a generative adversarial network learning a one-dimensional Gaussian distribution. The example can, however, easily be adapted to more complex cases and includes thorough comments.

11thAPRIL2017

## Reading/Writing JSON and HDF5 in Lua/Torch

Reading and writing JSON and HDF5 in Lua/Torch using luajson and torch-hdf5.

08thAPRIL2017

## Convolutional Variational Auto-Encoder in Torch

An example of a convolutional variational auto-encoder for fixed-size rectangles in $24 \times 24$ images with different anchors; convolutional variant of the snippet presented here: Variational Auto-Encoder in Torch. The variational auto-encoder is able to learn a $2$-dimensional code shown in the interpolations below the listing. The example can easily be adapted to more complex data.

08thAPRIL2017

## Variational Auto-Encoder in Torch

An example of a variational auto-encoder for fixed-size rectangles in $24 \times 24$ images with different anchors. The variational auto-encoder is able to learn a $2$-dimensional code shown in the interpolations below the listing. The example can easily be adapted to more complex data.

## Examples for Getting Started with Torch for Deep Learning

This article is a collection of Torch examples meant as introduction to get started with Lua and Torch for deep learning research. The examples can also be considered individually and cover common use cases such as training on CPU and GPU, weight initialization and visualization, custom modules and criteria as well as saving and fine-tuning models.

18thMARCH2017

## Example of Fine-Tuning in Torch

An example of fine-tuning an auto-encoder for classification. The example demonstrates how arbitrary modules can easily be extended to fix the weights and/or biases after loading a model. Additionally it shows how weights and biases can manually be copied between models with a different structure.

14thMARCH2017

## Defining Torch Modules on Custom Data Structures

Minimal example of defining a custom Torch module on a custom data structure. This example defines a simple data structure wrapping two Torch tensors and defines a linear nn.Module to operate on this data structure. While the backward pass is not implemented, the example illustrates how Torch can be extended for deep learning on custom data structures.

11thMARCH2017

## Custom Abs Criterion in Torch

A simple custom abs-criterion in Torch extending nn.Criterion.

09thMARCH2017

## Manual Weight and Bias Initialization in Torch

Simple LUA package to manually initialize the weights and biases of a network in Torch according to different strategies — these include uniform and normal initialization as well as heuristic and Xavier initialization. The package is easily extended to include additional initialization schemes and allows to initialize weights and biases using different strategies.

09thMARCH2017

## Convolutional Auto-Encoder using Torch’s optim Package

A simple convolutional auto-encoder implemented in Torch and trained using Torch’s optim package.