nninit
Parameter initialisation schemes for Torch7 neural network modules. Works with nn, and therefore nngraph. Allows arbitrary indexing of weights/biases/parameters. Supported modules:
- nn.Linear / nn.LinearNoBias
- nn.LookupTable
- nn.TemporalConvolution
- nn.SpatialConvolution / cudnn.SpatialConvolution
- nn.VolumetricConvolution / cudnn.VolumetricConvolution
Readme contents:
Installation
luarocks install nninitUsage
nninit adds an init method to nn.Module, with the following API:
module:init(accessor, initialiser, ...)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. nninit comes with several initialiser functions. ... represents additional arguments for the initialiser function.
Accessors
The accessor argument is used to extract the tensor to be initialised from the module. It can either be a string, table, or function.
string
The tensor is accessed as a property of the module. For example:
module:init('weight', nninit.constant, 1)table
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. For example:
module:init({'weight', {{1, 5}, {}}}, nninit.uniform, -1, 1)function
The tensor must be returned as the result of the function applied to the module. For example:
module:init(function(m) return m.weight:narrow(1, 1, 10) end, nninit.normal, 0, 0.01)Initialisers
nninit.copy(module, tensor, init)
Copies the init tensor to the tensor to be initialised.
nninit.constant(module, tensor, val)
Fills tensor with the constant val.
nninit.addConstant(module, tensor, val)
Adds to current tensor with the constant val.
nninit.mulConstant(module, tensor, val)
Multiplies current tensor by the constant val.
nninit.normal(module, tensor, mean, stdv)
Fills tensor ~ N(mean, stdv).
nninit.addNormal(module, tensor, mean, stdv)
Adds to current tensor with ~ N(mean, stdv).
nninit.uniform(module, tensor, a, b)
Fills tensor ~ U(a, b).
nninit.addUniform(module, tensor, a, b)
Adds to current tensor with ~ U(a, b).
nninit.eye(module, tensor)
Only supports the module weights as the tensor. Relies on the module type to determine appropriate identity.
Fills weights with the identity matrix (for linear layers/lookup tables).
Fills filters with the Dirac delta function (for convolutional layers). Normalises by the number of input layers.
nninit.xavier(module, tensor, [{[dist], [gain]}])
Fills tensor with stdv = gain * sqrt(2 / (fanIn + fanOut)). Uses the uniform distribution by default.
Optional named parameters dist and gain can be passed in via a table.
Also known as Glorot initialisation.
Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In International Conference on Artificial Intelligence and Statistics.
nninit.kaiming(module, tensor, [{[dist], [gain]}])
Fills tensor with stdv = gain * sqrt(1 / fanIn). Uses the normal distribution by default.
Optional named parameters dist and gain can be passed in via a table. 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 known as He initialisation.
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. arXiv preprint arXiv:1502.01852.
nninit.orthogonal(module, tensor, [{[gain]}])
Only supports tensors with at least 2 dimensions.
Fills tensor with a (normally distributed) random orthogonal matrix.
Optional named parameter gain can be passed in via a table.
Saxe, A. M., McClelland, J. L., & Ganguli, S. (2013). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120.
nninit.sparse(module, tensor, sparsity)
Sets (1 - sparsity) percent of the tensor to 0, where sparsity is between 0 and 1. For example, a sparsity of 0.2 drops out 80% of the tensor.
Martens, J. (2010). Deep learning via Hessian-free optimization. In Proceedings of the 27th International Conference on Machine Learning (ICML-10).
nninit.convolutionAware(module, tensor, [{[gain], [std]}])
Only supports 2D convolutions with a symmetric filter size.
Fills convolution tensor with matrices that are orthogonal in the frequency space.
The initialisation scheme described in the paper includes the gain for ReLU units, which has to be manually specified with the option {gain = 'relu'}.
The optional named parameter std can be passed in via a table. It specifies the noise to break symmetry in the inverse Fourier transform.
Aghajanyan, A. (2017). Convolution Aware Initialization. arXiv preprint arXiv:1702.06295.
Dists
The 2 types of distribution supported are 'normal' and 'uniform'.
Gains
Gains can be calculated depending on the succeeding nonlinearity. If gain is a number it is used directly; if gain is a string the following mapping is used. By default gains (where applicable) are set to 1.
| Gain | Parameters | Mapping |
|---|---|---|
| 'linear' | 1 | |
| 'sigmoid' | 1 | |
| 'tanh' | 5 / 3 | |
| 'relu' | sqrt(2) | |
| 'lrelu' | leakiness | sqrt(2 / (1 + leakiness^2)) |
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:
module:init('weight', nninit.kaiming, {gain = {'lrelu', leakiness = 0.3}})Example
local nn = require 'nn'
require 'cunn'
local cudnn = require 'cudnn'
require 'rnn'
local nninit = require 'nninit'
local getBias = function(module)
return module.bias
end
local batchSize = 5
local imgSize = 16
local nChannels = 3
local nFilters = 8
local rho = 6
local hiddenSize = 2
local cnn = nn.Sequential()
cnn:add(cudnn.SpatialConvolution(nChannels, nFilters, 2, 2):init('weight', nninit.eye)
:init('weight', nninit.mulConstant, 1/2)
:init('weight', nninit.addNormal, 0, 0.01)
:init(getBias, nninit.constant, 0))
cnn:add(nn.View(nFilters*15*15))
cnn:add(nn.Linear(nFilters*15*15, nFilters):init('weight', nninit.kaiming, {
dist = 'uniform',
gain = {'lrelu', leakiness = 0.3}
}))
cnn:add(nn.RReLU(1/3, 1/3))
cnn:add(nn.Linear(nFilters, 6):init('weight', nninit.orthogonal, {gain = 'relu'}))
cnn:add(cudnn.ReLU())
cnn:add(nn.Linear(6, 4):init('weight', nninit.xavier, {dist = 'normal', gain = 1.1}))
cnn:add(nn.Linear(4, hiddenSize):init('weight', nninit.sparse, 0.2)
:init(getBias, nninit.constant, 0))
local model = nn.Sequential()
model:add(nn.Sequencer(cnn))
local lstm = nn.FastLSTM(hiddenSize, hiddenSize, rho)
-- Note that chaining will pass through the module initialised, never parents
lstm.i2g:init({'bias', {{2*hiddenSize+1, 3*hiddenSize}}}, nninit.constant, 1) -- High forget gate bias
model:add(nn.Sequencer(lstm))
model:cuda()
local inputs = {}
for i = 1, rho do
table.insert(inputs, torch.ones(batchSize, nChannels, imgSize, imgSize):cuda())
end
print(model:forward(inputs))Development
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.