Brian Broll
09b6ca0ad0
WIP #3. Added import tests WIP #3 Added more test-cases WIP #3 Added more tests WIP #3. Fixed concat.lua test WIP #3 minor changes WIP #3 Fixed concat-parallel.lua WIP #3 Added check-model helper WIP #3 Added more tests for model checker WIP #3 Added extra tests WIP #3 Changed check-model to GraphChecker WIP #3. multiple cases fail for ImportTorch... WIP #3 Fixed ImportTorch batch test case running WIP #3 Changed graph checker to use gme path for id WIP #3 Updated tests WIP #3. Tweaked to get all examples working locally w/ 'th' WIP #3 Fixed tests
28 linhas
898 B
Lua
28 linhas
898 B
Lua
require 'nn'
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local nfeats = 500
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local nstates = {}
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local filtsize = 10
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local poolsize = 10
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-- a typical modern convolution network (conv+relu+pool)
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model = nn.Sequential()
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-- stage 1 : filter bank -> squashing -> L2 pooling -> normalization
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model:add(nn.SpatialConvolutionMM(nfeats, nstates[1], filtsize, filtsize))
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model:add(nn.ReLU())
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model:add(nn.SpatialMaxPooling(poolsize,poolsize,poolsize,poolsize))
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-- stage 2 : filter bank -> squashing -> L2 pooling -> normalization
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model:add(nn.SpatialConvolutionMM(nstates[1], nstates[2], filtsize, filtsize))
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model:add(nn.ReLU())
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model:add(nn.SpatialMaxPooling(poolsize,poolsize,poolsize,poolsize))
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-- stage 3 : standard 2-layer neural network
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model:add(nn.View(nstates[2]*filtsize*filtsize))
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model:add(nn.Dropout(0.5))
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model:add(nn.Linear(nstates[2]*filtsize*filtsize, nstates[3]))
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model:add(nn.ReLU())
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model:add(nn.Linear(nstates[3], noutputs))
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