├── .gitignore ├── LICENSE ├── README.md ├── common.lua ├── load_midi.lua ├── plot_stats.lua ├── rbm.lua ├── sample.lua └── train.lua /.gitignore: -------------------------------------------------------------------------------- 1 | # Compiled Lua sources 2 | luac.out 3 | 4 | # luarocks build files 5 | *.src.rock 6 | *.zip 7 | *.tar.gz 8 | 9 | # Object files 10 | *.o 11 | *.os 12 | *.ko 13 | *.obj 14 | *.elf 15 | 16 | # Precompiled Headers 17 | *.gch 18 | *.pch 19 | 20 | # Libraries 21 | *.lib 22 | *.a 23 | *.la 24 | *.lo 25 | *.def 26 | *.exp 27 | 28 | # Shared objects (inc. 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See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # elevator-music-generator 2 | This is my Master's thesis project about generative recurrent neural network written in Torch7. 3 | -------------------------------------------------------------------------------- /common.lua: -------------------------------------------------------------------------------- 1 | -- common.lua 2 | -- Zdeněk Janeček, 2016 (ycdmdj@gmail.com) 3 | -- 4 | -- University of West Bohemia 5 | 6 | function momentum_update(derivate, velocity, target, config) 7 | --~ derivate:mul(-config.learning_rate) 8 | 9 | velocity:mul(config.momentum):add(derivate) 10 | target:add(velocity) 11 | end 12 | 13 | function sample_ber(x) 14 | local r = torch.rand(x:size()) 15 | if torch.type(x) == 'torch.ClTensor' then 16 | r = r:cl() 17 | elseif torch.type(x) == 'torch.CudaTensor' then 18 | r = r:cuda() 19 | end 20 | 21 | x:csub(r):sign():clamp(0, 1) 22 | return x 23 | end 24 | 25 | function sparsity_update(rbm, qold, input, config) 26 | local target = torch.Tensor(1) 27 | 28 | if config.opencl then 29 | target = target:cl() 30 | end 31 | 32 | -- get moving average of last value and current 33 | local qcurrent = rbm.mu1:mean(1)[1] 34 | qcurrent:mul(1-config.sparsity_decay_rate) 35 | qcurrent:add(config.sparsity_decay_rate, qold) 36 | qold:copy(qcurrent) 37 | 38 | target:resizeAs(qcurrent) 39 | target:fill(config.sparsity_target) 40 | local diffP = qcurrent:csub(target) 41 | local dP_dW = torch.ger(diffP, input:mean(1)[1]) 42 | 43 | rbm.weight:csub(dP_dW:mul(config.sparsity_cost)) 44 | rbm.hbias:csub(diffP:mul(config.sparsity_cost)) 45 | end 46 | -------------------------------------------------------------------------------- /load_midi.lua: -------------------------------------------------------------------------------- 1 | -- load_midi.lua 2 | -- Zdeněk Janeček, 2016 (ycdmdj@gmail.com) 3 | -- 4 | -- University of West Bohemia 5 | 6 | MIDI=require 'MIDI' 7 | require 'paths' 8 | 9 | input_pool = {} 10 | 11 | roll_height = 88 12 | number_count = 0 13 | 14 | function init_pool(conf) 15 | for fname in paths.files(conf.data_dir) do 16 | if fname ~= '..' and fname ~= '.' then 17 | local piano_roll = load_song(conf.data_dir..'/'..fname) 18 | 19 | for t=1, piano_roll:size(2) do 20 | -- input 21 | local input = torch.ByteTensor(roll_height) 22 | input:copy(piano_roll[{{}, t}]) 23 | table.insert(input_pool, input) 24 | 25 | if #input_pool%(conf.batch_size+conf.rho-1)==0 then 26 | number_count = number_count + 1 27 | end 28 | end 29 | end 30 | end 31 | end 32 | 33 | function load_batch(number, batch, conf) 34 | assert(number<=number_count, "non existing batch") 35 | 36 | -- because indexing from 1 sucks 37 | local begin = (number-1)*#batch+1 38 | 39 | for t=0, #batch-1 do 40 | for i=0, conf.batch_size-1 do 41 | batch[t+1][i+1]:copy(input_pool[begin+t+i]) 42 | end 43 | end 44 | end 45 | 46 | function load_song(name) 47 | local f=assert(io.open(name, 'r')) 48 | local score=MIDI.midi2score(f:read('*all')) 49 | local stats=MIDI.score2stats(score) 50 | f:close() 51 | 52 | -- read only assigned tracks 53 | local assigned = {} 54 | for key, val in pairs(stats['channels_by_track']) do 55 | if #val > 0 then 56 | table.insert(assigned, key+1) 57 | end 58 | end 59 | 60 | local division_4 = score[1] 61 | local division_32 = division_4 / 4 62 | local song_len = stats['nticks'] 63 | 64 | local song_bitmap = torch.ByteTensor(roll_height, math.ceil(song_len / division_32)+2):zero() 65 | 66 | -- for each track with music 67 | for key, track in pairs(assigned) do 68 | -- for each event 69 | for k,event in pairs(score[track]) do 70 | -- if it is note, not drum on channel 10 and regular instrument 71 | if event[1] == 'note' and event[4] ~= 10 and event[5] < 112 then 72 | start = math.ceil(event[2] / division_32) + 1 73 | duration = math.ceil(event[3] / division_32) 74 | note = event[5] 75 | 76 | if note >= 21 and note <= 108 then 77 | song_bitmap[{note-20, {start, start+duration}}] = 1 78 | end 79 | end 80 | end 81 | end 82 | 83 | return song_bitmap 84 | end 85 | -------------------------------------------------------------------------------- /plot_stats.lua: -------------------------------------------------------------------------------- 1 | -- plot_stats.lua 2 | -- Zdeněk Janeček, 2016 (ycdmdj@gmail.com) 3 | -- 4 | -- University of West Bohemia 5 | 6 | require 'gnuplot' 7 | 8 | function draw_stats(values, name) 9 | keys = {'weight', 'vbias', 'hbias', 'weightVelocity', 'vbiasVelocity', 'hbiasVelocity'} 10 | for _, v in pairs(keys) do 11 | gnuplot.pngfigure('images/'..v..'-'..name..'.png') 12 | gnuplot.raw('set yrange [0.1:*]') 13 | --gnuplot.raw('set ytics ("0" 0.1, "1" 1, "10" 10)') 14 | gnuplot.raw('set logscale y 2') 15 | gnuplot.xlabel(string.format('%.6f', torch.abs(values[v]):mean())) 16 | gnuplot.hist(values[v], 500) 17 | gnuplot.plotflush() 18 | end 19 | end 20 | 21 | function draw_sc(t, name) 22 | gnuplot.pngfigure('images/'..name..'.png') 23 | gnuplot.imagesc(t) 24 | gnuplot.plotflush() 25 | end 26 | 27 | function draw_hist(t, name, label) 28 | gnuplot.pngfigure('images/'..name..'.png') 29 | gnuplot.xlabel(label) 30 | gnuplot.ylabel('četnost') 31 | gnuplot.hist(t) 32 | gnuplot.plotflush() 33 | end 34 | 35 | function draw_plot(t, name) 36 | gnuplot.pngfigure('images/'..name..'.png') 37 | gnuplot.plot(t) 38 | gnuplot.plotflush() 39 | end 40 | -------------------------------------------------------------------------------- /rbm.lua: -------------------------------------------------------------------------------- 1 | -- rbm.lua 2 | -- Zdeněk Janeček, 2016 (ycdmdj@gmail.com) 3 | -- 4 | -- University of West Bohemia 5 | 6 | require 'common' 7 | require 'nn' 8 | 9 | local RBM, parent = torch.class('RBM', 'nn.Module') 10 | 11 | function RBM:__init(n_visible, n_hidden, batch_size) 12 | parent.__init(self) 13 | self.n_visible = n_visible 14 | self.n_hidden = n_hidden 15 | 16 | self.output = torch.Tensor() 17 | self.input = torch.Tensor() 18 | 19 | self.weight = torch.Tensor(self.n_hidden, self.n_visible) 20 | self.vbias = torch.zeros(self.n_visible) 21 | self.hbias = torch.zeros(self.n_hidden) 22 | 23 | self.gradWeight = torch.zeros(self.n_hidden, self.n_visible) 24 | self.gradVbias = torch.zeros(self.n_visible) 25 | self.gradHbias = torch.zeros(self.n_hidden) 26 | if batch_size ~= nil then 27 | self.gradVbiasBatch = torch.zeros(batch_size, self.n_visible) 28 | self.gradHbiasBatch = torch.zeros(batch_size, self.n_hidden) 29 | end 30 | 31 | self.buffer = torch.Tensor() 32 | 33 | self.posGrad = torch.zeros(self.n_hidden, self.n_visible) 34 | self.negGrad = torch.zeros(self.n_hidden, self.n_visible) 35 | 36 | self.mu1 = torch.Tensor() 37 | self.vt = torch.Tensor() 38 | 39 | self.cdSteps = 15 40 | 41 | self:reset() 42 | end 43 | 44 | -- propup 45 | function RBM:updateOutputExpected(v_t) 46 | if v_t:dim() == 1 then 47 | self.output:resize(self.weight:size(1)) 48 | self.output:copy(self.hbias) 49 | self.output:addmv(1, self.weight, v_t) 50 | if self.hbiaslt ~= nil then self.output:add(self.hbiaslt) end 51 | elseif v_t:dim() == 2 then 52 | local nframe = v_t:size(1) 53 | self.output:resize(nframe, self.weight:size(1)) 54 | self.output:mm(v_t, self.weight:t()) 55 | self.output:add(self.hbias:view(1, self.n_hidden) 56 | :expand(nframe, self.n_hidden)) 57 | if self.hbiaslt ~= nil then 58 | self.output:add(self.hbiaslt) 59 | end 60 | else 61 | error('input must be vector or matrix') 62 | end 63 | 64 | return self.output:sigmoid() 65 | end 66 | 67 | -- propdown 68 | function RBM:updateInputExpected(h_t) 69 | if h_t:dim() == 1 then 70 | self.input:resize(self.weight:size(2)) 71 | self.input:copy(self.vbias) 72 | self.input:addmv(1, self.weight:t(), h_t) 73 | if self.vbiaslt ~= nil then self.input:add(self.vbiaslt) end 74 | elseif h_t:dim() == 2 then 75 | local nframe = h_t:size(1) 76 | self.input:resize(nframe, self.weight:size(2)) 77 | self.input:mm(h_t, self.weight) 78 | self.input:add(self.vbias:view(1, self.n_visible) 79 | :expand(nframe, self.n_visible)) 80 | if self.vbiaslt ~= nil then 81 | self.input:add(self.vbiaslt) 82 | end 83 | else 84 | error('hidden must be vector or matrix') 85 | end 86 | 87 | return self.input:sigmoid() 88 | end 89 | 90 | -- returns probability of visible activations 91 | function RBM:gibbs(visible) 92 | local vt = visible 93 | for t=1, self.cdSteps-1 do 94 | ht = sample_ber(self:updateOutputExpected(vt)) 95 | vt = sample_ber(self:updateInputExpected(ht)) 96 | end 97 | 98 | ht = sample_ber(self:updateOutputExpected(vt)) 99 | vt = self:updateInputExpected(ht) 100 | 101 | self.vt:resizeAs(vt) 102 | self.vt:copy(vt) 103 | sample_ber(self.vt) 104 | 105 | return vt 106 | end 107 | 108 | function RBM:updateOutput(input) 109 | if torch.type(input) == 'table' then 110 | self.input:resizeAs(input[1]) 111 | self.input:copy(input[1]) 112 | self.vbiaslt = input[2] 113 | self.hbiaslt = input[3] 114 | else 115 | self.input:resizeAs(input) 116 | self.input:copy(input) 117 | self.vbiaslt = nil 118 | self.hbiaslt = nil 119 | end 120 | 121 | return self:gibbs(self.input) 122 | end 123 | 124 | -- we don't need gratOutput in unsupervised greedy training 125 | function RBM:updateGradInput(input) 126 | if torch.type(input) == 'table' then 127 | self.input:resizeAs(input[1]) 128 | self.input:copy(input[1]) 129 | self.vbiaslt = input[2] 130 | self.hbiaslt = input[3] 131 | else 132 | self.input:resizeAs(input) 133 | self.input:copy(input) 134 | self.vbiaslt = nil 135 | self.hbiaslt = nil 136 | end 137 | 138 | local v1 = self.input 139 | local vt = self.vt 140 | 141 | local mu1 = self:updateOutputExpected(v1) 142 | self.mu1:resizeAs(mu1) 143 | self.mu1:copy(mu1) 144 | 145 | local mut = self:updateOutputExpected(vt) 146 | 147 | if v1:dim() == 1 then 148 | torch.ger(self.posGrad, self.mu1, v1) 149 | torch.ger(self.negGrad, mut, vt) 150 | 151 | -- update gradients 152 | torch.csub(self.gradWeight, self.negGrad, self.posGrad) 153 | torch.csub(self.gradVbias, vt, v1) 154 | torch.csub(self.gradHbias, mut, self.mu1) 155 | 156 | self.gradInput = {torch.Tensor(), self.gradVbias, self.gradHbias} 157 | elseif v1:dim() == 2 then 158 | local nframe = v1:size(1) 159 | 160 | torch.mm(self.posGrad, self.mu1:t(), v1) 161 | torch.mm(self.negGrad, mut:t(), vt) 162 | 163 | -- update gradients 164 | torch.csub(self.gradWeight, self.negGrad, self.posGrad) 165 | self.gradWeight:div(nframe) 166 | 167 | torch.csub(self.gradVbiasBatch, vt, v1) 168 | torch.mean(self.gradVbias, self.gradVbiasBatch, 1) 169 | 170 | torch.csub(self.gradHbiasBatch, mut, self.mu1) 171 | torch.mean(self.gradHbias, self.gradHbiasBatch, 1) 172 | 173 | self.gradInput = {torch.Tensor(), self.gradVbiasBatch, self.gradHbiasBatch} 174 | else 175 | error('input must be vector or matrix') 176 | end 177 | 178 | return self.gradInput 179 | end 180 | 181 | function RBM:freeEnergy(visible) 182 | if visible:dim() == 1 then 183 | self.output:resize(self.n_hidden) 184 | self.output:copy(self.hbias) 185 | self.output:addmv(1, self.weight, visible):exp():add(1):log() 186 | local neg = self.output:sum() 187 | local pos = torch.dot(visible, self.vbias) 188 | return -neg-pos 189 | elseif visible:dim() == 2 then 190 | local nframe = visible:size(1) 191 | self.output:resize(nframe, self.n_hidden) 192 | self.output:mm(visible, self.weight:t()) 193 | self.output:add(self.hbias:view(1, self.n_hidden) 194 | :expand(nframe, self.n_hidden)) 195 | self.output:exp():add(1):log() 196 | local neg = self.output:sum(2) 197 | local pos = torch.mv(visible, self.vbias) 198 | return (-neg-pos):sum() 199 | end 200 | end 201 | 202 | function RBM:reset() 203 | self.weight:normal(0, 0.08) 204 | 205 | self.gradWeight:zero() 206 | self.gradHbias:zero() 207 | self.gradVbias:zero() 208 | self.gradHbiasBatch:zero() 209 | self.gradVbiasBatch:zero() 210 | end 211 | 212 | function RBM:parameters() 213 | return {self.weight, self.vbias, self.hbias}, 214 | {self.gradWeight, self.gradVbias, self.gradHbias} 215 | end 216 | -------------------------------------------------------------------------------- /sample.lua: -------------------------------------------------------------------------------- 1 | -- sample.lua 2 | -- Zdeněk Janeček, 2016 (ycdmdj@gmail.com) 3 | -- 4 | -- University of West Bohemia 5 | 6 | require 'load_midi' 7 | require 'rbm' 8 | require 'nn' 9 | require 'rnn' 10 | require 'gnuplot' 11 | 12 | MIDI=require 'MIDI' 13 | 14 | cmd = torch.CmdLine() 15 | cmd:text() 16 | cmd:text('Sample from the NN-RBM music generator model') 17 | cmd:text() 18 | cmd:text('Options') 19 | -- data 20 | cmd:option('-data_dir','test','data directory. Should contain MIDI files.') 21 | -- model params 22 | cmd:option('-rnn_model','models/recurrence-rnn_10.dat','Recurrent module') 23 | cmd:option('-mlp_model','models/recurrence-mlp_10.dat','MLP module with bias and RBM part') 24 | 25 | cmd:option('-n_hidden', 150, 'RBM hidden layer size.') 26 | cmd:option('-n_recurrent', 100, 'Recurrent hidden size.') 27 | 28 | cmd:option('-length',300,'sample length') 29 | cmd:option('-o', 'sampled.mid', 'output mid file') 30 | 31 | opt = cmd:parse(arg) 32 | torch.seed() 33 | 34 | opt.n_visible = roll_height 35 | opt.batch_size = 1 36 | opt.rho = 1 37 | 38 | init_pool(opt) 39 | 40 | rnn = torch.load(opt.rnn_model).module 41 | mlp = torch.load(opt.mlp_model).module 42 | 43 | rnn:forget() 44 | mlp:forget() 45 | rnn:evaluate() 46 | mlp:evaluate() 47 | 48 | piano_roll = torch.Tensor(opt.length, opt.n_visible) 49 | zeros = torch.zeros(opt.n_visible) 50 | 51 | rnn_outputs={} 52 | rnn_outputs[0] = rnn:forward(input_pool[torch.random(1,#input_pool)]:double()) 53 | 54 | -- initial sequence 55 | for t=1, opt.length do 56 | local sampled_v = mlp:forward{zeros, rnn_outputs[t-1]} 57 | rnn_outputs[t] = rnn:forward(sampled_v) 58 | 59 | piano_roll[t]:copy(sampled_v) 60 | end 61 | 62 | gnuplot.pngfigure(opt.o..'.png') 63 | gnuplot.imagesc(piano_roll:t()) 64 | gnuplot.plotflush() 65 | 66 | ticks = 96 67 | division_4 = ticks 68 | division_32 = division_4 / 4 69 | 70 | score = {ticks, {}} 71 | score[2][1] = {"patch_change", 0, 0, 0} 72 | score[2][2] = {'set_tempo', 0, 1100000} 73 | 74 | counter = 3 75 | for t=1, opt.length do 76 | for note=1, roll_height do 77 | if piano_roll[t][note] == 1 then 78 | -- trace note and erase 79 | local trace = t 80 | while trace < opt.length-1 and (piano_roll[trace+1][note] == 1 or piano_roll[trace+2][note] == 1) do 81 | trace = trace + 1 82 | end 83 | local duration = trace - t + 1 84 | piano_roll[{{t, trace}, note}]:fill(0) 85 | 86 | target_note = note + 20 87 | if target_note >= 0 and target_note <= 127 then 88 | score[2][counter] = {"note", (t-1)*division_32, duration*division_32, 0, target_note, 90} 89 | counter = counter + 1 90 | end 91 | end 92 | end 93 | end 94 | 95 | midifile = assert(io.open(opt.o,'w')) 96 | midifile:write(MIDI.score2midi(score)) 97 | midifile:close() 98 | -------------------------------------------------------------------------------- /train.lua: -------------------------------------------------------------------------------- 1 | -- train.lua 2 | -- Zdeněk Janeček, 2016 (ycdmdj@gmail.com) 3 | -- 4 | -- University of West Bohemia 5 | 6 | require 'load_midi' 7 | require 'common' 8 | require 'paths' 9 | require 'plot_stats' 10 | require 'optim' 11 | 12 | cmd = torch.CmdLine() 13 | cmd:text() 14 | cmd:text('Train a RNN-RBM music generator model') 15 | cmd:text() 16 | cmd:text('Options') 17 | -- data 18 | cmd:option('-data_dir','train','data directory. Should contain MIDI files.') 19 | cmd:option('-prefix', '', 'prefix of this run') 20 | cmd:option('-v', false, 'verbose mode') 21 | 22 | -- model params 23 | cmd:text('Model parameters') 24 | cmd:option('-n_hidden', 150, 'RBM hidden layer size.') 25 | cmd:option('-n_recurrent', 100, 'Recurrent hidden size.') 26 | cmd:option('-model', 'lstm', 'lstm or gru') 27 | cmd:option('-init_rbm_from', '', 'initialize RBM from this file') 28 | 29 | cmd:text('Optimalization parameters') 30 | -- optimization 31 | cmd:option('-learning_rate',0.018,'learning rate') 32 | cmd:option('-momentum',0.5,'momentum') 33 | cmd:option('-L1',0.0008,'L1 decay') 34 | cmd:option('-max_pretrain_epochs', 140, 'number of full passes through the training data while RBM pretrain') 35 | 36 | cmd:option('-sparsity_decay_rate',0.9,'decay rate for sparsity') 37 | cmd:option('-sparsity_target',0.08,'sparsity target') 38 | cmd:option('-sparsity_cost',0.0006,'sparsity cost') 39 | 40 | cmd:option('-sgd_learning_rate',0.004,'learning rate for SGD') 41 | cmd:option('-sgd_learning_rate_decay',0.97,'learning rate decay') 42 | cmd:option('-sgd_learning_rate_decay_after',40,'in number of epochs, when to start decaying the learning rate') 43 | cmd:option('-max_epochs',80,'number of full passes through the training data') 44 | 45 | cmd:option('-rho',32,'number of timesteps to unroll for') 46 | cmd:option('-batch_size',100,'number of sequences to train on in parallel') 47 | cmd:option('-stat_interval',256,'statistics interval') 48 | cmd:option('-opencl', false,'use OpenCL backend') 49 | cmd:option('-cuda', false,'use CUDA backend') 50 | 51 | opt = cmd:parse(arg) 52 | torch.seed() 53 | 54 | --~ opt.rmsprop_epsilon = 1e-8 55 | opt.n_visible = roll_height 56 | 57 | init_pool(opt) 58 | 59 | -- Split dataset 60 | input = torch.DoubleTensor(opt.batch_size, roll_height) 61 | 62 | test = {} 63 | train = {} 64 | 65 | -- 512 is enough 66 | number_count = math.min(512, number_count) 67 | train_size = math.floor(0.7*number_count) 68 | test_size = number_count - train_size 69 | 70 | print('Train sample size', train_size) 71 | print('Test sample size', test_size) 72 | 73 | for i=1, test_size do 74 | load_batch(train_size+i, {input}, opt) 75 | test[i] = input:clone() 76 | end 77 | for i=1, train_size do 78 | load_batch(i, {input}, opt) 79 | train[i] = input:clone() 80 | end 81 | 82 | if opt.opencl then 83 | require 'cltorch' 84 | require 'clnn' 85 | 86 | for i=1, #test do 87 | test[i] = test[i]:cl() 88 | end 89 | for i=1, #train do 90 | train[i] = train[i]:cl() 91 | end 92 | elseif opt.cuda then 93 | require 'cutorch' 94 | require 'cunn' 95 | 96 | for i=1, #test do 97 | test[i] = test[i]:cuda() 98 | end 99 | for i=1, #train do 100 | train[i] = train[i]:cuda() 101 | end 102 | else 103 | require 'nn' 104 | end 105 | 106 | require 'rbm' 107 | require 'rnn' 108 | 109 | function reconstruction_test() 110 | local err = 0 111 | 112 | for i=1, test_size do 113 | local v1 = test[i] 114 | rbm:gibbs(v1) 115 | local v2 = rbm.vt 116 | local diff = torch.csub(v1, v2) 117 | err = err + diff:abs():mean() 118 | end 119 | 120 | return (err/test_size)*100 121 | end 122 | 123 | function reconstruction_train() 124 | local err = 0 125 | 126 | for i=1, train_size do 127 | local v1 = train[i] 128 | rbm:gibbs(v1) 129 | local v2 = rbm.vt 130 | local diff = torch.csub(v1, v2) 131 | err = err + diff:abs():mean() 132 | end 133 | 134 | return (err/train_size)*100 135 | end 136 | 137 | function free_energy_test() 138 | local err = 0 139 | local v1 = torch.Tensor(28*28) 140 | 141 | for i=1, test_size do 142 | err = err + rbm:freeEnergy(test[i]) 143 | end 144 | 145 | return err/test_size 146 | end 147 | 148 | function free_energy_train() 149 | local err = 0 150 | local v1 = torch.Tensor(28*28) 151 | 152 | for i=1, train_size do 153 | err = err + rbm:freeEnergy(train[i]) 154 | end 155 | 156 | return err/train_size 157 | end 158 | 159 | -- 1) Run RBM pretrain 160 | criterion = nn.BCECriterion() 161 | 162 | function pretrain_feval(t) 163 | local visible = train[t] 164 | 165 | local pred = rbm:forward(visible) 166 | local err = criterion:forward(pred, visible) 167 | rbm:backward(visible) 168 | 169 | dl_dx:mul(-opt.learning_rate) 170 | 171 | L1 = torch.sign(rbm.gradWeight) 172 | rbm.gradWeight:add(opt.L1, L1) 173 | 174 | momentum_update(dl_dx, velocity, x, opt) 175 | sparsity_update(rbm, qval, visible, opt) 176 | 177 | return err 178 | end 179 | 180 | if string.len(opt.init_rbm_from) > 0 then 181 | rbmPretrained = torch.load(opt.init_rbm_from) 182 | rbm = RBM(opt.n_visible, opt.n_hidden, opt.batch_size) 183 | rbm.weight = rbmPretrained.weight 184 | rbm.vbias = rbmPretrained.vbias 185 | rbm.hbias = rbmPretrained.hbias 186 | 187 | print('Loaded model from', opt.init_rbm_from) 188 | else 189 | -- Create RBM 190 | rbm = RBM(opt.n_visible, opt.n_hidden, opt.batch_size) 191 | 192 | -- Training parameters 193 | weightVelocity = rbm.gradWeight:clone() 194 | vbiasVelocity = rbm.gradVbias:clone() 195 | hbiasVelocity = rbm.gradHbias:clone() 196 | 197 | qval = torch.zeros(opt.n_hidden, 1) 198 | 199 | if opt.opencl then 200 | criterion = criterion:cl() 201 | rbm = rbm:cl() 202 | weightVelocity = weightVelocity:cl() 203 | vbiasVelocity = vbiasVelocity:cl() 204 | hbiasVelocity = hbiasVelocity:cl() 205 | qval = qval:cl() 206 | end 207 | if opt.cuda then 208 | criterion = criterion:cuda() 209 | rbm = rbm:cuda() 210 | weightVelocity = weightVelocity:cuda() 211 | vbiasVelocity = vbiasVelocity:cuda() 212 | hbiasVelocity = hbiasVelocity:cuda() 213 | qval = qval:cuda() 214 | end 215 | 216 | velocity = nn.Module.flatten{weightVelocity, vbiasVelocity, hbiasVelocity} 217 | x,dl_dx = rbm:getParameters() 218 | 219 | histogramValues = { 220 | weight = rbm.weight, 221 | vbias = rbm.vbias, 222 | hbias = rbm.hbias, 223 | 224 | weightVelocity = weightVelocity, 225 | vbiasVelocity = vbiasVelocity, 226 | hbiasVelocity = hbiasVelocity 227 | } 228 | 229 | err = 0; iter = 0 230 | for epoch=1, opt.max_pretrain_epochs do 231 | print('pretrain epoch '..epoch) 232 | 233 | velocity:zero() 234 | 235 | if epoch == math.floor(opt.max_pretrain_epochs*0.5) then 236 | torch.save('models/'..opt.prefix..'pretrained_rbm_'..epoch..'.dat', rbm) 237 | opt.momentum = 0.8 238 | end 239 | if epoch == math.floor(opt.max_pretrain_epochs*0.72) then 240 | opt.momentum = 0.9 241 | end 242 | if epoch == opt.max_pretrain_epochs then 243 | torch.save('models/'..opt.prefix..'pretrained_rbm_'..epoch..'.dat', rbm) 244 | end 245 | 246 | for t = 1, train_size do 247 | iter = iter + 1 248 | 249 | err = err + pretrain_feval(t) 250 | 251 | if iter >= opt.stat_interval then 252 | local test = reconstruction_test(rbm) 253 | local train = reconstruction_train(rbm) 254 | local energy_test = free_energy_test(rbm) 255 | local energy_train = free_energy_train(rbm) 256 | 257 | print(string.format('%s t=%d loss=%.4f test=%.4f%% train=%.4f%% ftest=%.4f ftrain=%.4f', os.date("%d/%m %H:%M:%S"), t, -err/opt.stat_interval, test, train, energy_test, energy_train)) 258 | 259 | -- reset counters 260 | err = 0; iter = 0 261 | 262 | if opt.v then 263 | draw_hist(rbm.mu1:mean(1), 'mean_hidden-'..epoch..'-'..t, 'pravděpodobnost') 264 | end 265 | end 266 | end 267 | 268 | if opt.v then 269 | draw_stats(histogramValues, 'hist_'..epoch) 270 | 271 | gnuplot.pngfigure('images/weight-map_'..epoch..'.png') 272 | gnuplot.ylabel('skryté') 273 | gnuplot.xlabel('viditelné') 274 | gnuplot.imagesc(rbm.weight) 275 | gnuplot.plotflush() 276 | end 277 | end 278 | end 279 | 280 | -- 2) finetune recurrence 281 | -- TODO: migrate to SeqLSTM and tensor inputs(opt.rho, opt.batch_size, opt.n_visible) 282 | 283 | if opt.model == 'lstm' then 284 | rnn_inner = nn.FastLSTM(opt.n_visible, opt.n_recurrent, opt.rho-1) 285 | elseif opt.model == 'gru' then 286 | rnn_inner = nn.GRU(opt.n_visible, opt.n_recurrent, opt.rho-1) 287 | else 288 | error("invalid model type") 289 | end 290 | 291 | -- {input(t), output(t-1)} -> outputV(t) 292 | mlp_inner = nn.Sequential() 293 | :add(nn.ParallelTable() 294 | :add(nn.Identity()) 295 | :add( 296 | nn.ConcatTable() 297 | :add(nn.LinearNoBias(opt.n_recurrent, opt.n_visible)) 298 | :add(nn.LinearNoBias(opt.n_recurrent, opt.n_hidden)) 299 | ) 300 | ) 301 | :add(nn.FlattenTable()) 302 | :add(rbm) 303 | 304 | rnn = nn.Sequencer(rnn_inner, opt.rho-1) 305 | mlp = nn.Sequencer(mlp_inner, opt.rho-1) 306 | 307 | inputs = {} 308 | 309 | if opt.opencl then 310 | rnn = rnn:cl() 311 | mlp = mlp:cl() 312 | 313 | for t=1, opt.rho do 314 | inputs[t] = torch.Tensor(opt.batch_size, roll_height):cl() 315 | end 316 | elseif opt.cuda then 317 | rnn = rnn:cuda() 318 | mlp = mlp:cuda() 319 | 320 | for t=1, opt.rho do 321 | inputs[t] = torch.Tensor(opt.batch_size, roll_height):cuda() 322 | end 323 | else 324 | for t=1, opt.rho do 325 | inputs[t] = torch.Tensor(opt.batch_size, roll_height) 326 | end 327 | end 328 | 329 | params, gradParam = nn.Container():add(rnn):add(mlp):getParameters() 330 | 331 | rnn_learning_rate = opt.sgd_learning_rate 332 | 333 | function fine_feval(x_new) 334 | if params ~= x_new then 335 | params:copy(x_new) 336 | end 337 | 338 | load_batch(batch_number, inputs, opt) 339 | batch_number = batch_number + 1 340 | 341 | gradParam:zero() 342 | 343 | local rnn_inputs = {} 344 | local mlp_inputs = {} 345 | 346 | for i=1, #inputs-1 do 347 | rnn_inputs[i]=inputs[i] 348 | end 349 | 350 | -- 1) prop rnn 351 | local rnn_outputs = rnn:forward(rnn_inputs) 352 | 353 | -- 2) generate negative phase of RBM 354 | for i=2, #inputs do 355 | mlp_inputs[i-1]={inputs[i], rnn_outputs[i-1]} 356 | end 357 | 358 | mlp:forward(mlp_inputs) 359 | 360 | -- 3) backprop rbm gradients 361 | local mlp_grads = mlp:backward(mlp_inputs, mlp_inputs) 362 | 363 | -- 4) backprop through time rnn gradients 364 | local mlp_grads_hid = {} 365 | for i=1, #mlp_grads do 366 | mlp_grads_hid[i] = mlp_grads[i][2] 367 | end 368 | 369 | rnn:backward(rnn_inputs, mlp_grads_hid) 370 | 371 | return _, gradParam 372 | end 373 | 374 | -- returns: likelihood, 375 | function evaluate() 376 | local zeros = torch.zeros(opt.batch_size, opt.n_visible) 377 | 378 | mlp:evaluate() 379 | rnn:evaluate() 380 | 381 | likelihood = 0 382 | precision = 0 383 | recall = 0 384 | accuracy = 0 385 | 386 | local rnn_output = rnn_inner:forward(zeros) 387 | for i=1, test_size do 388 | local pred = mlp_inner:forward{zeros, rnn_output} 389 | rnn_output = rnn_inner:forward(test[i]) 390 | 391 | if pred:ne(pred):sum() > 0 then 392 | print(sys.COLORS.red .. ' prediction has NaN/s') 393 | end 394 | 395 | if rnn_output:ne(rnn_output):sum() > 0 then 396 | print(sys.COLORS.red .. ' hidden rnn has NaN/s') 397 | end 398 | 399 | likelihood = likelihood + criterion:forward(pred, test[i]) 400 | 401 | pred_ber = mlp_inner:get(3).vt 402 | 403 | local TP = torch.cmul(pred_ber, test[i]):sum() 404 | local FP = torch.cmul(pred_ber:byte(), torch.eq(test[i], 0)):sum() 405 | local FN = torch.cmul(torch.eq(pred_ber, 0), test[i]:byte()):sum() 406 | 407 | precision = precision + TP / (TP + FP) 408 | recall = recall + TP / (TP + FN) 409 | accuracy = accuracy + TP / (TP + FP + FN) 410 | end 411 | 412 | mlp:training() 413 | rnn:training() 414 | 415 | likelihood = likelihood / test_size 416 | precision = 100 * precision / test_size 417 | recall = 100 * recall / test_size 418 | accuracy = 100 * accuracy / test_size 419 | 420 | fmeasure = (2*precision*recall)/(precision + recall) 421 | 422 | return likelihood, precision, recall, accuracy, fmeasure 423 | end 424 | 425 | paths.mkdir('models') 426 | 427 | mlp:training() 428 | rnn:training() 429 | 430 | for epoch=1, opt.max_epochs do 431 | print('finetune epoch '..epoch) 432 | batch_number = 1 433 | 434 | if epoch % 4 == 0 and epoch >= opt.sgd_learning_rate_decay_after then 435 | rnn_learning_rate = rnn_learning_rate * opt.sgd_learning_rate_decay 436 | print('decayed learning rate by a factor ' .. opt.sgd_learning_rate_decay .. ' to ' .. rnn_learning_rate) 437 | end 438 | 439 | local conf = { 440 | learningRate = rnn_learning_rate, 441 | alpha = opt.sgd_learning_rate_decay 442 | } 443 | 444 | for t = 1, train_size do 445 | optim.rmsprop(fine_feval, params, conf) 446 | xlua.progress(t, train_size) 447 | 448 | if params:ne(params):sum() > 0 then 449 | print(sys.COLORS.red .. ' network params has NaN/s') 450 | end 451 | end 452 | 453 | likelihood, precision, recall, accuracy, fmeasure = evaluate() 454 | 455 | print(string.format(' log-likelihood=%.4f', -likelihood)) 456 | print(string.format(' Precision=%.4f', precision)) 457 | print(string.format(' Recall=%.4f', recall)) 458 | print(string.format(' Accuracy=%.4f', accuracy)) 459 | print(string.format(' F-measure=%.4f', fmeasure)) 460 | 461 | if epoch % 10 == 0 then 462 | rnn:forget() 463 | mlp:forget() 464 | 465 | torch.save('models/'..opt.prefix..'recurrence-rnn_'..epoch..'.dat', rnn) 466 | torch.save('models/'..opt.prefix..'recurrence-mlp_'..epoch..'.dat', mlp) 467 | end 468 | end 469 | 470 | rnn:forget() 471 | mlp:forget() 472 | --------------------------------------------------------------------------------