deprecated dp and Recurrent and FastLSTM

examples/noise-contrastive-estimate.lua

@@ -221,7 +221,6 @@ while opt.maxepoch <= 0 or epoch <= opt.maxepoch do
       sumErr = sumErr + err
       -- backward
       local gradOutputs = criterion:backward(outputs, targets)
-      local a = torch.Timer()
       lm:zeroGradParameters()
       lm:backward(inputs, gradOutputs)
 

examples/recurrent-language-model.lua

@@ -94,20 +94,19 @@ for i,hiddensize in ipairs(opt.hiddensize) do
    if opt.gru then -- Gated Recurrent Units
       rnn = nn.GRU(inputsize, hiddensize, nil, opt.dropout/2)
    elseif opt.lstm then -- Long Short Term Memory units
-      require 'nngraph'
-      nn.FastLSTM.usenngraph = true -- faster
-      nn.FastLSTM.bn = opt.bn
-      rnn = nn.FastLSTM(inputsize, hiddensize)
+      rnn = nn.RecLSTM(inputsize, hiddensize)
    elseif opt.mfru then -- Multi Function Recurrent Unit
       rnn = nn.MuFuRu(inputsize, hiddensize)
-   else -- simple recurrent neural network
+   elseif i == 1 then -- simple recurrent neural network
       local rm =  nn.Sequential() -- input is {x[t], h[t-1]}
          :add(nn.ParallelTable()
-            :add(i==1 and nn.Identity() or nn.Linear(inputsize, hiddensize)) -- input layer
+            :add(nn.Identity()) -- input layer
             :add(nn.Linear(hiddensize, hiddensize))) -- recurrent layer
          :add(nn.CAddTable()) -- merge
          :add(nn.Sigmoid()) -- transfer
       rnn = nn.Recurrence(rm, hiddensize, 1)
+   else
+      rnn = nn.LinearRNN(hiddensize, hiddensize)
    end
 
    stepmodule:add(rnn)

examples/recurrent-time-series.lua

@@ -1,11 +1,11 @@
--- Multi-variate time-series example 
+-- Multi-variate time-series example
 
 require 'rnn'
 
 cmd = torch.CmdLine()
 cmd:text()
 cmd:text('Train a multivariate time-series model using RNN')
-cmd:option('--rho', 5, 'maximum number of time steps for back-propagate through time (BPTT)')
+cmd:option('--seqlen', 5, 'maximum number of time steps for back-propagate through time (BPTT)')
 cmd:option('--multiSize', 6, 'number of random variables as input and output')
 cmd:option('--hiddenSize', 10, 'number of hidden units used at output of the recurrent layer')
 cmd:option('--dataSize', 100, 'total number of time-steps in dataset')
@@ -53,20 +53,12 @@ print('Sequence:'); print(sequence)
 
 offsets = torch.LongTensor(opt.batchSize):random(1,opt.dataSize)
 
--- RNN
-r = nn.Recurrent(
-   opt.hiddenSize, -- size of output
-   nn.Linear(opt.multiSize, opt.hiddenSize), -- input layer
-   nn.Linear(opt.hiddenSize, opt.hiddenSize), -- recurrent layer
-   nn.Sigmoid(), -- transfer function
-   opt.rho
-)
-
+-- Simple RNN
 rnn = nn.Sequential()
-   :add(r)
+   :add(nn.LinearRNN(opt.multiSize, opt.hiddenSize))
    :add(nn.Linear(opt.hiddenSize, opt.multiSize))
 
-criterion = nn.MSECriterion() 
+criterion = nn.MSECriterion()
 
 -- use Sequencer for better data handling
 rnn = nn.Sequencer(rnn)
@@ -79,12 +71,12 @@ print(rnn)
 minErr = opt.multiSize -- report min error
 minK = 0
 avgErrs = torch.Tensor(opt.nIterations):fill(0)
-for k = 1, opt.nIterations do 
+for k = 1, opt.nIterations do
+
+   -- 1. create a sequence of seqlen time-steps
 
-   -- 1. create a sequence of rho time-steps
-
    local inputs, targets = {}, {}
-   for step = 1, opt.rho do
+   for step = 1, opt.seqlen do
       -- batch of inputs
       inputs[step] = inputs[step] or sequence.new()
       inputs[step]:index(sequence, 1, offsets)
@@ -99,10 +91,10 @@ for k = 1, opt.nIterations do
 
    local outputs = rnn:forward(inputs)
    local err = criterion:forward(outputs, targets)
-   
+
    -- report errors
-   
-   print('Iter: ' .. k .. '   Err: ' .. err)   
+
+   print('Iter: ' .. k .. '   Err: ' .. err)
    if opt.plot then
       logger:add{['Err'] = err}
       logger:style{['Err'] = '-'}
@@ -116,14 +108,14 @@ for k = 1, opt.nIterations do
    end
 
    -- 3. backward sequence through rnn (i.e. backprop through time)
-   
+
    rnn:zeroGradParameters()
-   
+
    local gradOutputs = criterion:backward(outputs, targets)
    local gradInputs = rnn:backward(inputs, gradOutputs)
 
    -- 4. updates parameters
-   
+
    rnn:updateParameters(opt.learningRate)
 end