AI News, Kaixhin/nninit


Parameter initialisation schemes for Torch7 neural network modules.

Supported modules: Readme contents: nninit adds an init method to nn.Module, with the following API: The accessor argument is used to extract the tensor to be initialised from the module.

The initialiser argument is a function that takes the module, tensor, and further options;

it adjusts the tensor and returns the module, allowing init calls to be chained.

For example: The tensor is first accessed as a property of the module from the first element, and a subtensor is then extracted using Torch's indexing operator applied to the second element.

The initialisation scheme typically includes the gain for ReLU units, which has to be manually specified in nninit.kaiming with the option {gain = 'relu'}. Also

Sets (1 - sparsity) percent of the tensor to 0, where sparsity is between 0 and 1.

initialisation scheme described in the paper includes the gain for ReLU units, which has to be manually specified with the option {gain = 'relu'}. The

If the gain must be calculated from additional parameters, gain must be passed as table with the string as the first element as well as named parameters.

For example: To develop nninit/use it to test new initialisation schemes, git clone/download this repo and use luarocks make rocks/nninit-scm-1.rockspec to install nninit locally.

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