favre49 / examples Goto Github PK

In mlpack-model-zoo project, we provide state of the art pre-built models and solutions to some standard datasets.

1. Introduction
2. Dependencies
3. Building-From-Source
4. Running Models
5. Current Models
6. Datasets

This repository contains a number of different models implemented in C++ using mlpack. To understand more about mlpack refer to the following links. The sample folder contains snippets and smaller models that demonstrate features of mlpack.

• mlpack homepage
• mlpack documentation
• Tutorials

To run this project you need:

  mlpack
  Armadillo     >= 8.400.0
  Boost (program_options, math_c99, unit_test_framework, serialization,
         spirit)
  CMake         >= 3.3.2
  ensmallen     >= 2.10.0

To install mlpack refer to the install guide.

All of those should be available in your distribution's package manager. If not, you will have to compile each of them by hand. See the documentation for each of those packages for more information.

To install this project run the following command.

mkdir build && cd build && cmake ../

Use the optional command -D DEBUG=ON to enable debugging.

Run the make file. Use -jN with the following command where N is the number of cores to be used for the build. For instance,

make -j4

After building the projects all datasets will be unzipped and executables of the model will be made available in bin. You can either execute the same file using:

./bin/fileName

Currently model-zoo project has the following models implemented:

• Simple Convolutional Neural Network on MNIST dataset.
• Multivariate Time Series prediction using LSTM on Google Stock Prices.
• Univariate Time Series prediction using LSTM on Electricity Consumption Dataset.
• Variational Auto-Encoder on MNIST dataset.
• Variational Convolutional Auto-Encoder on MNIST.

Model-Zoo project has the following datasets available:

MNIST("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel- value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel- value is an integer between 0 and 255, inclusive. The training data set, (train.csv), has 785 columns. The first column, called "label", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image. For more information refer to this MNIST Database.

Google Stock-Prices Dataset consists of stock prices for each day from 27th June, 2016 to 27th June, 2019. Each tuple is seperated from its adjacent tuple by 1 day. It consists of following rows that indicate opening, closing, volume and high and low of stocks associated with Google on that day.

Contains electricity consumption of a city for 2011 to 2012, where each tuple is seperated from its adjacent tuple by 1 day.
Each tuple has consumption in kWH and binary values for each Off-peak, Mid-peak, On-peak rows.