Ladder

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Ladder is an open-source deep learning algorithm that combines supervised and unsupervised learning. It provides source code for experiments in semi-supervised learning with Ladder Networks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ladder?

Ladder network is a deep learning algorithm that combines both supervised and unsupervised learning techniques. This repository provides the source code for experiments detailed in the paper "Semi-Supervised Learning with Ladder Networks." It requires specific libraries like Theano, Blocks Stable 0.2, and Fuel Stable 0.2 for installation and operation. The tool includes various models for MNIST and CIFAR datasets, allowing users to train full, bottom-only, gamma, and supervised baseline models with different configurations for labeled and unlabeled samples. It also supports evaluating trained models on test sets.

Best used for

Ideal for data scientists and developers who need to implement and experiment with semi-supervised learning algorithms, train models on datasets like MNIST and CIFAR, and evaluate their performance. Especially valuable for researchers and practitioners exploring deep learning techniques.

Common actions

implement deep learning models

conduct machine learning experiments

train semi-supervised models

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Capabilities

Key features

Semi-supervised learning algorithm
MNIST model training
CIFAR model training
Configurable sample sizes
Model evaluation

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary libraries required to run Ladder?

To run Ladder, you need to install Theano, Blocks Stable 0.2, and Fuel Stable 0.2. The repository provides instructions for installing these dependencies, including specific commands for pip and conda environments to set up the necessary components.

Can Ladder be used for both MNIST and CIFAR datasets?

Yes, the Ladder repository includes commands and configurations for training models on both MNIST and CIFAR datasets. It provides examples for various model architectures, including fully connected and convolutional networks, tailored for each dataset.

How can I evaluate a trained model using Ladder?

After training a model, you can evaluate its performance on a test set using the 'evaluate' command provided in the run.py script. An example command is given in the README, demonstrating how to specify the results file of a previously trained model.

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