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- Torch.mean(input, dim, keepdim=False,., out=None) → Tensor Returns the mean value of each row of the input tensor in the given dimension dim. If dim is a list of dimensions, reduce over all of them. If keepdim is True, the output tensor is of the same size as input except in the dimension (s) dim where it is of size 1.
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- Tensorminexample = torch.tensor ( 1,-10, 1, 2, 2, 2, 3, 3, 3 4, 4, 4, 5,50, 5, 6, 6, 6 ) We use torch.tensor to create a floating point tensor. We pass in our data structure which is going to be 2x3x3, and we assign it to the Python variable tensorminexample.
| Original author(s) | Ronan Collobert, Samy Bengio, Johnny Mariéthoz[1] |
|---|---|
| Initial release | October 2002; 18 years ago[1] |
| Stable release | |
| Repository | |
| Written in | Lua, LuaJIT, C, CUDA and C++ |
| Operating system | Linux, Android, Mac OS X, iOS |
| Type | Library for machine learning and deep learning |
| License | BSD License |
| Website | torch.ch |
Torch is an open-sourcemachine learning library, a scientific computing framework, and a script language based on the Lua programming language.[3] It provides a wide range of algorithms for deep learning, and uses the scripting language LuaJIT, and an underlying C implementation. As of 2018, Torch is no longer in active development.[4] However PyTorch, which is based on the Torch library, is actively developed as of December 2020.[5]
torch[edit]
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, matrix-vector product and matrix product.
The following exemplifies using torch via its REPL interpreter:
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.
nn[edit]
The nn package is used for building neural networks. It is divided into modular objects that share a common Module interface. Modules have a forward() and backward() method that allow them to feedforward and backpropagate, respectively. Modules can be joined together using module composites, like Sequential, Parallel and Concat to create complex task-tailored graphs. Simpler modules like Linear, Tanh and Max make up the basic component modules. This modular interface provides first-order automatic gradient differentiation. What follows is an example use-case for building a multilayer perceptron using Modules:
Loss functions are implemented as sub-classes of Criterion, which has a similar interface to Module. It also has forward() and backward() methods for computing the loss and backpropagating gradients, respectively. Criteria are helpful to train neural network on classical tasks. Common criteria are the Mean Squared Error criterion implemented in MSECriterion and the cross-entropy criterion implemented in ClassNLLCriterion. What follows is an example of a Lua function that can be iteratively called to train an mlp Module on input Tensor x, target Tensor y with a scalar learningRate:
It also has StochasticGradient class for training a neural network using Stochastic gradient descent, although the optim package provides much more options in this respect, like momentum and weight decay regularization.
Other packages[edit]
Many packages other than the above official packages are used with Torch. These are listed in the torch cheatsheet.[6] These extra packages provide a wide range of utilities such as parallelism, asynchronous input/output, image processing, and so on. They can be installed with LuaRocks, the Lua package manager which is also included with the Torch distribution.
Applications[edit]
Torch Mini
Torch is used by the Facebook AI Research Group,[7]IBM,[8]Yandex[9] and the Idiap Research Institute.[10] Torch has been extended for use on Android[11] and iOS.[12] It has been used to build hardware implementations for data flows like those found in neural networks.[13]
Facebook has released a set of extension modules as open source software.[14]
See also[edit]
References[edit]
- ^ ab'Torch: a modular machine learning software library'. 30 October 2002. CiteSeerX10.1.1.8.9850.Cite journal requires
|journal=(help) - ^Collobert, Ronan. 'Torch7'. GitHub.
- ^'Torch7: A Matlab-like Environment for Machine Learning'(PDF). Neural Information Processing Systems. 2011.
- ^Torch GitHub repository ReadMe
- ^PyTorch GitHub repository
- ^'Cheatsheet · torch/torch7 Wiki'.
- ^KDnuggets Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab
- ^Hacker News
- ^Yann Lecun's Facebook Page
- ^IDIAP Research Institute : Torch
- ^Torch-android GitHub repository
- ^Torch-ios GitHub repository
- ^NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision
- ^'Facebook Open-Sources a Trove of AI Tools'. Wired. 16 January 2015.
External links[edit]
Clamp all elements in input into the range [min, max].Let min_value and max_value be min and max, respectively, this returns:
input (Tensor) – the input tensor.
min (Number) – lower-bound of the range to be clamped to
Download targus group int driver. max (Number) – upper-bound of the range to be clamped to
out (Tensor, optional) – the output tensor.
Example:
torch.clamp(input, *, min, out=None) → Tensor
Clamps all elements in input to be larger or equal min.
input (Tensor) – the input tensor.
min (Number) – minimal value of each element in the output
out (Tensor, optional) – the output tensor.
Example:
Torch Mini-batch
torch.clamp(input, *, max, out=None) → Tensor
Clamps all elements in input to be smaller or equal max.
Torch Minecraft Picture
input (Tensor) – the input tensor.
Download trinityworks input devices driver. max (Number) – maximal value of each element in the output
out (Tensor, optional) – the output tensor.
Example: