simgt · June 23, 2016 12:43 · Jun 23, 2016
diff --git a/rnn-sine-waves.lua b/rnn-sine-waves.lua
@@ -0,0 +1,115 @@
+local torch =require 'torch'
+local nn =require 'nn'
+local rnn =require 'rnn'
+local gnuplot = require 'gnuplot'
+
+torch.setnumthreads(4)
+print('number of threads: ' .. torch.getnumthreads())
+
+batchSize = 16
+rho = 16 -- sequence length
+lr = 0.01
+
+--
+-- Model
+--
+
+inputSize = 1
+hiddenSize = 32
+outputSize = 1
+
+local r = nn.Recurrent(
+   hiddenSize, -- start
+   nn.Linear(inputSize, hiddenSize), -- input
+   nn.Linear(hiddenSize, hiddenSize), -- feedback
+   nn.Sigmoid(), -- transfer
+   rho
+)
+
+local rnn = nn.Sequential()
+   :add(r)
+   :add(nn.Linear(hiddenSize, outputSize))
+
+rnn = nn.Sequencer(rnn)
+
+-- load a model previously trained
+--rnn = torch.load('sine-waves-model.dat', 'ascii', true)
+
+print(rnn)
+
+criterion = nn.SequencerCriterion(nn.MSECriterion())
+
+--
+-- Dataset
+--
+
+local numSamples = 1024
+local numPeriods = 10
+
+local t = torch.linspace(0, numPeriods * 2 * math.pi, numSamples)
+local input = torch.Tensor(numSamples, inputSize)
+local output = torch.Tensor(numSamples, outputSize)
+input:select(2, 1):copy(torch.sin(t))
+--input:select(2, 2):copy(torch.sin(t/2))
+output:select(2, 1):copy(torch.sin(t/2))
+--output:select(2, 2):copy(torch.sin(t*2))
+
+--
+-- Training
+--
+
+local it = 1
+while true do
+    offsets = torch.LongTensor(batchSize)
+    for i=1,batchSize do
+       offsets[i] = math.ceil(math.random()*input:size(1))
+    end
+
+    for a = 1, 2000 do
+        -- create a batch of sequences of rho time-steps
+        local x, y = {}, {}
+        for step = 1, rho do
+            x[step] = input:index(1, offsets)
+            y[step] = output:index(1, offsets)
+
+            -- incement indices
+            offsets = offsets + 1
+            for j = 1, batchSize do
+                if offsets[j] > numSamples then
+                    offsets[j] = 1
+                end
+            end
+        end
+
+        -- forward the sequence
+        local z = rnn:forward(x)
+        local err = criterion:forward(z, y)
+
+        print(string.format("[%d] err = %f", it, err / rho))
+
+        -- backward the sequence (i.e. BPTT) in reverse order of forward calls
+        rnn:zeroGradParameters()
+        local gz = criterion:backward(z, y)
+        rnn:backward(x, gz)
+
+        -- update
+        rnn:updateParameters(lr)
+
+        it = it + 1
+    end
+
+    -- save the model
+    print("Saving...")
+    torch.save('sine-waves-model.dat', rnn, 'ascii', true)
+
+    -- test on the full sequence
+    local z = rnn:forward(input)
+
+    gnuplot.pngfigure('sine-waves-test.png')
+    gnuplot.plot(
+        {'input', t, input:select(2, 1), '-'},
+        {'truth', t, output:select(2, 1), '-'},
+        {'estimate', t, z:select(2, 1), '-'}
+    )
+    gnuplot.plotflush()
+end