All types torch.DoubleTensor, torch.FloatTensor, etc. should have their
sparse variants: torch.SparseDoubleTensor, torch.SparseFloatTensor, etc.
Copying between dense and sparse matrix should be done with :copy() function.
Underlying BLAS has to be swappable with MKL/OpenBLAS/Atlas, etc. Other math operations have to implemented with CSPARSE.
Install Atom from https://atom.io or with brew cask install atom
Then install packages (example apm install hydrogen, apm is a package manager for atom):
| local Linear, parent = torch.class('nn.NoBiasLinear', 'nn.Linear') | |
| function Linear:__init(inputSize, outputSize) | |
| parent.__init(self, inputSize, outputSize) | |
| self.bias:fill(0) | |
| end | |
| function Linear:accGradParameters(input, gradOutput, scale) | |
| scale = scale or 1 | |
| if input:dim() == 1 then |
| -- a script to simplify trained net by incorporating every SpatialBatchNormalization to SpatialConvolution | |
| -- and BatchNormalization to Linear | |
| local function BNtoConv(net) | |
| for i,v in ipairs(net.modules) do | |
| if v.modules then | |
| BNtoConv(v) | |
| else | |
| if torch.typename(v) == 'nn.SpatialBatchNormalization' and | |
| (torch.typename(net:get(i-1)):find'SpatialConvolution') then |
| require 'nn' | |
| local vgg = nn.Sequential() | |
| -- building block | |
| local function ConvBNReLU(nInputPlane, nOutputPlane) | |
| vgg:add(nn.SpatialConvolution(nInputPlane, nOutputPlane, 3,3, 1,1, 1,1)) | |
| vgg:add(nn.SpatialBatchNormalization(nOutputPlane,1e-3)) | |
| vgg:add(nn.ReLU(true)) | |
| return vgg | |
| end |
| local BidirectionalSequencer, parent = torch.class('nn.BidirectionalSequencer', 'nn.Container') | |
| function BidirectionalSequencer:__init(module_forward, module_backward, nOutputSize) | |
| parent.__init(self) | |
| self.module_forward = module_forward | |
| self.module_backward = module_backward | |
| self.modules[1] = nn.Sequencer(module_forward) | |
| self.modules[2] = nn.Sequencer(module_backward) | |
| self.output = {} |
| Using 1-th gpu | |
| Loading ./data/ptb.train.txt, size of data = 929589 | |
| Loading ./data/ptb.valid.txt, size of data = 73760 | |
| Loading ./data/ptb.test.txt, size of data = 82430 | |
| Network parameters: | |
| { | |
| layers : 2 | |
| lr : 1 | |
| max_max_epoch : 13 | |
| max_grad_norm : 5 |
| Using 1-th gpu | |
| Loading ./data/ptb.train.txt, size of data = 929589 | |
| Loading ./data/ptb.valid.txt, size of data = 73760 | |
| Loading ./data/ptb.test.txt, size of data = 82430 | |
| Network parameters: | |
| { | |
| layers : 2 | |
| lr : 1 | |
| max_max_epoch : 13 | |
| max_grad_norm : 5 |
| function removeDescriptors(net) | |
| for i,val in pairs(net.modules) do | |
| if tostring(val):find('cudnn') then | |
| for name,field in pairs(val) do | |
| if name:find('Desc') then | |
| val[name] = nil | |
| end | |
| end | |
| val.algType = nil | |
| val.iDesc = nil |
| #!/usr/bin/env python | |
| import subprocess | |
| import re | |
| class bcolors: | |
| HEADER = '\033[95m' | |
| OKBLUE = '\033[94m' | |
| OKGREEN = '\033[92m' | |
| WARNING = '\033[93m' |