Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Torch (machine learning)
Summary
Torch is an open-source machine learning library,
a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created at IDIAP at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python.
The core package of Torch is torch. It provides a flexible N-dimensional array or Tensor, which supports basic routines for indexing, slicing, transposing, type-casting, resizing, sharing storage and cloning. This object is used by most other packages and thus forms the core object of the library. The Tensor also supports mathematical operations like max, min, sum, statistical distributions like uniform, normal and multinomial, and BLAS operations like dot product, matrix–vector multiplication, matrix–matrix multiplication and matrix product.
The following exemplifies using torch via its REPL interpreter:
a = torch.randn(3,4) =a 0.2381 -0.3401 -1.7844 -0.2615 0.1411 1.6249 0.1708 0.8299 1.0434 2.2291 1.0525 0.8465 [torch.DoubleTensor of dimension 3x4] a[1][2] 0.34010116549482 a:narrow(1,1,2) 0.2381 -0.3401 -1.7844 -0.2615 0.1411 1.6249 0.1708 0.8299 [torch.DoubleTensor of dimension 2x4] a:index(1, torch.LongTensor{1,2}) 0.2381 -0.3401 -1.7844 -0.2615 0.1411 1.6249 0.1708 0.8299 [torch.DoubleTensor of dimension 2x4] a:min() 1.7844365427828 The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories (classes). When the constructor is called, torch initializes and sets a Lua table with the user-defined metatable, which makes the table an object. Objects created with the torch factory can also be serialized, as long as they do not contain references to objects that cannot be serialized, such as Lua coroutines, and Lua userdata. However, userdata can be serialized if it is wrapped by a table (or metatable) that provides read() and write() methods.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related publications (30)
Related units (1)
Related concepts (2)
Feedforward neural network
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.
Neural network
A neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.