#Maté

Maté is a MATLAB library for ConvNets (Convolutional Neural Networks). Maté is derived from MatConvNet. Maté uses MatConvNet routines for several time-critical operations and can load and manipulate pretrained networks supplied with MatConvNet.

Maté is object-oriented and has the goal to simplify prototyping and experimentation, in particular to minimize the efforts needed to try new non-standard layers and new non-standard network graphs.

Maté continues the tradition of naming a ConvNet library after a caffeine-related drink. Other such libraries that also inspired Maté include Decaf (Python, CPU) and Caffe (C++ with interfaces, CPU/GPU).

##Functionality Maté provides functionality for:

• Easy definition of new layers (just define forward and, if needed, backward procedures; it often takes couple of lines in MATLAB).
• Easy definition and training of non-chain networks (any directed acyclic graph can be used) within MATLAB code.
• Easy loading and manipulation of pretrained networks.
• Easy GPU mode thanks to MATLAB Parallel Toolbox and MatConvNet routines. For many (most?) new layers same MATLAB code can be reused for CPU and GPU modes.
• Easy visualization of the internal state of a network during training.

##Installation Install MatConvNet from here, clone this repository, and run setup.m (which simply adds subfolders to MATLAB path).

##Howto

• Build a network (+other basics)
• Use a pre-trained network
• Train a network
• Layer catalog
• Define a new layer

##Citation If you use Maté to prepare your publication, please cite MatConvNet report as suggested here.

##Status The project is in a very early prerelease state, use at your own risk.