Theano-lights：基于Theano的深度学习研究框架

Theano-Lights是一个基于Theano的深度学习研究框架，提供了Several recent Deep learning 模型实现和一个便利的训练和测试功能。The models are not hidden and spread out behind layers of abstraction as in most deep learning platforms to enable transparency and flexiblity during learning and research.

Included models:

• Feedforward neural network (FFN)
• Convolutional neural network (CNN)
• Recurrent neural networks (RNN)
• Variational autoencoder (VAE)
• Convolutional Variational autoencoder (CVAE)
• Deep Recurrent Attentive Writer (DRAW)
• LSTM language model

Included features:

• Batch normalization
• Dropout
• LSTM, GRU and SCRN recurrent layers
• Virtual adversarial training (Miyato et al., 2015)
• Contractive cost (Rifai et al., 2011)

Stochastic gradient descent variants:

• SGD with momentum
• SGD Langevin dynamics
• Rmsprop
• Adam
• Adam with gradient clipping
• Walk-forward learning for non-stationary data (data with concept drift)

Supervised training on:

• MNIST

Unsupervised training on:

• MNIST
• Frey Faces
• Penn Treebank
• text8

Other models and features:

• Auto-classifier-encoder (Georgiev, 2015)
• Radias basis function neural network
• Denoising autoencoder with lateral connections

Work in progress:

• Natural neural networks (Desjardins et al., 2015)
• Ladder network (Rasmus et al., 2015)
• Virtual adversarial training for CNN and RNN