itorch NTM.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "require 'dpnn'\n",
    "require \"cunn\"\n",
    "require \"nngraph\"\n",
    "require 'hdf5'\n",
    "require 'xlua'\n",
    "require 'optim'\n",
    "require 'itorch.Plot'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "local pre_file = hdf5.open('../dataminingcapstone/ntm40.hdf5', 'r')\n",
    "pre_w1 = pre_file:read('/w1'):all():transpose(1,2):type('torch.DoubleTensor')\n",
    "examples = pre_file:read('/examples'):all():add(1):transpose(1,2):type('torch.DoubleTensor')\n",
    "le_in = pre_file:read('/le'):all():transpose(1,2):type('torch.DoubleTensor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "local pre_file = hdf5.open('../dataminingcapstone/W1_pretrain_40.hdf5', 'r')\n",
    "pre_w2 = pre_file:read('/layer_2/param_0'):all():type('torch.DoubleTensor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "-- the gram stack\n",
    "gram_stack = nn.Sequential()\n",
    "le_start = nn.Dictionary(le_in:size()[1], le_in:size()[2])\n",
    "le_start.weight = le_in\n",
    "-- mark this not-trainable\n",
    "function le_start:updateParameters(learningRate)\n",
    "end\n",
    "gram_stack:add(le_start)\n",
    "gram_stack:add(nn.Reshape(le_in:size()[2], true))\n",
    "\n",
    "local lt1 = nn.Linear(pre_w2:size()[1], pre_w2:size()[2])\n",
    "lt1.weight = pre_w2:transpose(1,2)\n",
    "lt1.bias = torch.zeros(pre_w2:size()[2])\n",
    "-- No bias in the lt module\n",
    "function lt1:accUpdateGradParameters(input, gradOutput, lr)\n",
    "   local gradWeight = self.gradWeight\n",
    "   self.gradWeight = self.weight\n",
    "   self:accGradParameters(input, gradOutput, -lr)\n",
    "   self.gradWeight = gradWeight\n",
    "end\n",
    "\n",
    "function lt1:accGradParameters(input, gradOutput, scale)\n",
    "   scale = scale or 1\n",
    "   if input:dim() == 1 then\n",
    "      self.gradWeight:addr(scale, gradOutput, input)\n",
    "   elseif input:dim() == 2 then\n",
    "      self.gradWeight:addmm(scale, gradOutput:t(), input)\n",
    "   end\n",
    "end\n",
    "\n",
    "gram_stack:add(lt1)\n",
    "gram_stack:add(nn.Sigmoid())\n",
    "\n",
    "-- the document stack\n",
    "doc_stack = nn.Sequential()\n",
    "local ld_1 = nn.Dictionary(pre_w1:size()[1], pre_w1:size()[2])\n",
    "ld_1.weight = pre_w1\n",
    "doc_stack:add(ld_1)\n",
    "doc_stack:add(nn.SoftMax())\n",
    "doc_stack:add(nn.Reshape(2,pre_w1:size()[2],true))\n",
    "doc_stack:add(nn.SplitTable(2,3))\n",
    "-- the scoring stack\n",
    "\n",
    "\n",
    "din = nn.Identity()()\n",
    "ld_pos, ld_neg = doc_stack(din):split(2)\n",
    "\n",
    "ld_pos:annotate({name = 'ld_pos'})\n",
    "ld_neg:annotate({name = 'ld_neg'})\n",
    "\n",
    "g = nn.Identity()()\n",
    "lt = gram_stack({g}) \n",
    "\n",
    "ls_pos = nn.DotProduct()({lt, ld_pos})\n",
    "ls_pos:annotate({name = 'ls_pos'})\n",
    "ls_neg = nn.DotProduct()({lt, ld_neg})\n",
    "ls_neg:annotate({name = 'ls_neg'})\n",
    "\n",
    "ntm = nn.gModule({g, din}, {ls_pos, ls_neg})\n",
    "\n",
    "loss_out = nn.L1HingeEmbeddingCriterion(0.5):cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ntm:cuda();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Convert the input data to a dataset that can be used by sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_meta = {\n",
    "    __index = function( self, k ) \n",
    "--        local i = torch.LongTensor{k}\n",
    "        return {{self.g[k], self.d[k]}, -1} \n",
    "    end\n",
    "}\n",
    "\n",
    "input_data = {\n",
    "    g = examples:index(2, torch.LongTensor{2}), \n",
    "    d = examples:index(2, torch.LongTensor{1,3}),\n",
    "    size = function (self) \n",
    "        return self.g:size()[1]\n",
    "    end,\n",
    "    shuffle = function (self, len)\n",
    "        self.shuffledIndices = torch.randperm(self.g:size()[1], 'torch.LongTensor')\n",
    "        self.batchIndex = 1\n",
    "        self.g_batch = torch.DoubleTensor(len, self.g:size()[2])\n",
    "        self.d_batch = torch.DoubleTensor(len, self.d:size()[2])\n",
    "        \n",
    "        self.t_batch = torch.DoubleTensor(len, 1):fill(-1)\n",
    "        return {self.g_batch, self.d_batch}, self.t_batch\n",
    "    end,\n",
    "    next_batch = function (self, len) \n",
    "        local maxidx = math.min(self.batchIndex + len - 1, self:size())\n",
    "        local batchindices = self.shuffledIndices[{{self.batchIndex, maxidx}}]\n",
    "--        return {self.g:index(1, batchindices, \n",
    "--                self.d:index(1, batchindices))}\n",
    "        self.g_batch:index(self.g, 1, batchindices)\n",
    "        self.d_batch:index(self.d, 1, batchindices)\n",
    "        self.batchIndex = self.batchIndex + len\n",
    "        return {self.g_batch, self.d_batch}, self.t_batch\n",
    "                --{self.g[{{self.batchIndex - len, self.batchIndex}}],\n",
    "               -- self.d[{{self.batchIndex - len, self.batchIndex}}]}\n",
    "    end\n",
    "}\n",
    "setmetatable(input_data, input_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/html": [
       "<script type=\"text/javascript\">\n",
       "  $(function() {\n",
       "    $(\"#101be03d-6d14-448f-c497-326fdb773880\").html('<p>Progress:</p>'); // clear any previous plot in window_id     \n",
       "  });\n",
       "</script>\n",
       "<div id=\"101be03d-6d14-448f-c497-326fdb773880\"></div>\n"
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   "source": [
    "-- ignore this - its only used to test the loss function\n",
    "\n",
    "trainer = nn.StochasticGradient(ntm, loss_out)\n",
    "trainer.maxIteration = 1\n",
    "trainer.learningRate = 0.01\n",
    "n_examples = input_data:size()\n",
    "i = 0\n",
    "window = itorch.html(\"<p>Progress:</p>\")\n",
    "startTime = os.time()\n",
    "trainer.hookExample = function(self, example) \n",
    "    if i == 0 then\n",
    "        startTime = os.time()\n",
    "    end\n",
    "    \n",
    "    i = i + 1\n",
    "    \n",
    "    if (i - 1) % 10000 == 0 then\n",
    "--        xlua.progress(i, n_examples)\n",
    "        local percCompl = i / n_examples\n",
    "        local eta = ((os.time() - startTime) / percCompl) - (os.time() - startTime)\n",
    "        itorch.html(string.format('<p>Progress:</p><p>%d / %d [' .. \n",
    "                string.rep(\"=\", percCompl * 50) .. '>' .. \n",
    "                string.rep(\".\", 49 - (percCompl * 50)) .. '] - ETA: %d seconds - Loss: Unknown</p>', \n",
    "                    i, n_examples, eta),\n",
    "            window)\n",
    "    end\n",
    "end\n",
    "trainer.hookIteration = function(self, iteration, currentError) \n",
    "        itorch.html(string.format('<p>Iteration %d completed in %d seconds with average loss %.6f.</p>', \n",
    "                iteration, os.time() - startTime, currentError),\n",
    "            window)\n",
    "        window = itorch.html('<p>Progress:</p>')\n",
    "end\n",
    "trainer:train(input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "-- function for useful plotting interface\n",
    "smart_trainer = function(model, criterion,\n",
    "        input_data, n_classes,\n",
    "        batch_size, cv_split, class_train, max_epochs, \n",
    "        optimizer, optimizer_params, \n",
    "        l1, l2, chart)\n",
    " \n",
    "    local training_loss_history = {}\n",
    "    local validation_loss_history = {}\n",
    "\n",
    "    local function feval(x_in)\n",
    "        \n",
    "        local prediction = model:forward(inputs, targets)\n",
    "        local losses = criterion:forward(prediction, targets)\n",
    "        gradients:zero()\n",
    "        local df = criterion:backward(prediction, targets)\n",
    "        model:backward(inputs, df)\n",
    "        \n",
    "        -- regularize\n",
    "        local norm,sign = torch.norm, torch.sign\n",
    "        \n",
    "        local loss = 0\n",
    "        if type(losses) == 'number' then\n",
    "            loss = losses\n",
    "        else\n",
    "            for i=1,#losses do loss = loss + losses[i] end\n",
    "        end\n",
    "        \n",
    "        if l1 ~= 0 then\n",
    "            loss = loss + l1 * norm(x, 1)\n",
    "            gradients:add(sign(x):mul(l1))\n",
    "        end\n",
    "        if l2 ~= 0 then\n",
    "            loss = loss + l2 * norm(x,2)^2/2       \n",
    "            gradients:add( x:clone():mul(l2))\n",
    "        end\n",
    "        \n",
    "        -- accuracy matrix\n",
    "        if class_train then confusion:batchAdd(prediction, targets) end\n",
    "        \n",
    "--        gradients:div(n_batches)\n",
    "        \n",
    "        return loss, gradients\n",
    "    end\n",
    "\n",
    "    local epoch = 0\n",
    "    local max_idx = input_data:size() * (1 - cv_split)\n",
    "    local old_text = 'Progress:<br>'\n",
    "    local window = itorch.html(old_text)\n",
    "    \n",
    "    while true do\n",
    "        model:training()\n",
    "    \n",
    "        if class_train then\n",
    "            train_confusion = optim.ConfusionMatrix(n_classes)\n",
    "            val_confusion = optim.ConfusionMatrix(n_classes)\n",
    "        end\n",
    "        local startTime = os.time()\n",
    "        epoch = epoch + 1\n",
    "        local currentError = 0\n",
    "        \n",
    "        if class_train then \n",
    "            train_confusion:zero()\n",
    "            val_confusion:zero()\n",
    "        end\n",
    "\n",
    "        inputs,targets = input_data:shuffle(batch_size)\n",
    "        \n",
    "        x, gradients = model:getParameters()\n",
    "        \n",
    "        for batch_index=1, max_idx, batch_size do\n",
    "            collectgarbage()\n",
    "            inputs, targets = input_data:next_batch(math.min(batch_size, max_idx - batch_index - 1))\n",
    "\n",
    "            _, f_table = optimizer(feval, x, optimizer_params)\n",
    "\n",
    "            local thisLoss = 0\n",
    "            for i=1,#f_table do  thisLoss = thisLoss + f_table[i] end\n",
    "            \n",
    "            currentError = ((currentError * batch_index) + (thisLoss * batch_size)) / (batch_index + batch_size)\n",
    "\n",
    "            local percCompl = math.floor(50 * batch_index / max_idx)\n",
    "            local eta = ((os.time() - startTime) / (percCompl / 50)) - (os.time() - startTime)\n",
    "            itorch.html(old_text .. \n",
    "                string.format('[%d / %d] [' .. \n",
    "                    string.rep(\"=\", percCompl - 1) .. '>' .. \n",
    "                    string.rep(\".\", 49 - percCompl ) .. '] ETA: %d seconds - Batch Loss: %.6f - Avg. Epoch Loss: %.6f<br>', \n",
    "                        batch_index, input_data:size(), \n",
    "                        eta, \n",
    "                        thisLoss * batch_size, \n",
    "                        currentError * batch_size\n",
    "                ),\n",
    "                window) \n",
    "        end\n",
    "        table.insert(training_loss_history, currentError)\n",
    "        \n",
    "        -- validation\n",
    "        model:evaluate()\n",
    "        local validation_loss = 0\n",
    "        for batch_index=max_idx,input_data:size(),batch_size do \n",
    "            collectgarbage()\n",
    "            inputs, targets = input_data:next_batch(math.min(batch_size, \n",
    "                    input_data:size() - batch_index - 1))\n",
    "            local prediction = model:forward(inputs, targets)\n",
    "            local losses = criterion:forward(prediction, targets)\n",
    "            \n",
    "            if type(losses) == 'number' then\n",
    "                validation_loss = validation_loss + losses\n",
    "            else\n",
    "                for i=1,#losses do validation_loss = validation_loss + losses[i] end\n",
    "            end\n",
    "        end\n",
    "        table.insert(validation_loss_history, validation_loss)\n",
    "        -- report update\n",
    "        old_text = old_text .. string.format('Epoch %d completed in %d seconds with training avg loss %.8f - ' ..\n",
    "                                                'Val loss %.8f.<br>', \n",
    "                    epoch, os.time() - startTime, currentError, validation_loss)\n",
    "        itorch.html(old_text,\n",
    "                window)\n",
    "        if chart then \n",
    "            plot = Plot()\n",
    "            local x_vals = torch.linspace(1,epoch + 10)\n",
    "            plot:line(x_vals, training_loss_history, \n",
    "                'red', 'Training Loss')\n",
    "            plot:line(x_vals, validation_loss_history, \n",
    "                'blue', 'Validation Loss')\n",
    "            plot:xaxis('Training vs Validation Loss')\n",
    "            plot:legend(true)\n",
    "            plot:gfx()\n",
    "        end\n",
    "\n",
    "        if max_epochs and epoch >= max_epochs then\n",
    "            break\n",
    "        end\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
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    {
     "ename": "[string \"-- function for useful plotting interface...\"]:121: attempt to call global 'Plot' (a nil value)\nstack traceback:\n\t[string \"-- function for useful plotting interface...\"]:121: in function 'f'\n\t[string \"local f = function() return smart_trainer(ntm...\"]:5: in main chunk\n\t[C]: in function 'xpcall'\n\t/usr/local/share/lua/5.1/itorch/main.lua:179: in function </usr/local/share/lua/5.1/itorch/main.lua:143>\n\t/usr/local/share/lua/5.1/lzmq/poller.lua:75: in function 'poll'\n\t/usr/local/share/lua/5.1/lzmq/impl/loop.lua:307: in function 'poll'\n\t/usr/local/share/lua/5.1/lzmq/impl/loop.lua:325: in function 'sleep_ex'\n\t/usr/local/share/lua/5.1/lzmq/impl/loop.lua:370: in function 'start'\n\t/usr/local/share/lua/5.1/itorch/main.lua:350: in main chunk\n\t[C]: in function 'require'\n\t(command line):1: in main chunk\n\t[C]: at 0x010ee74b70",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "[string \"-- function for useful plotting interface...\"]:121: attempt to call global 'Plot' (a nil value)\nstack traceback:\n\t[string \"-- function for useful plotting interface...\"]:121: in function 'f'\n\t[string \"local f = function() return smart_trainer(ntm...\"]:5: in main chunk\n\t[C]: in function 'xpcall'\n\t/usr/local/share/lua/5.1/itorch/main.lua:179: in function </usr/local/share/lua/5.1/itorch/main.lua:143>\n\t/usr/local/share/lua/5.1/lzmq/poller.lua:75: in function 'poll'\n\t/usr/local/share/lua/5.1/lzmq/impl/loop.lua:307: in function 'poll'\n\t/usr/local/share/lua/5.1/lzmq/impl/loop.lua:325: in function 'sleep_ex'\n\t/usr/local/share/lua/5.1/lzmq/impl/loop.lua:370: in function 'start'\n\t/usr/local/share/lua/5.1/itorch/main.lua:350: in main chunk\n\t[C]: in function 'require'\n\t(command line):1: in main chunk\n\t[C]: at 0x010ee74b70"
     ]
    }
   ],
   "source": [
    "smart_trainer(ntm, loss_out,\n",
    "        input_data, nil,\n",
    "        10000, 0.2, false, 500, \n",
    "        optim.sgd, {learningRate = 0.01}, \n",
    "        0, 0.001, false)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "collectgarbage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "input_data:shuffle(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "this_batch, targets = input_data:next_batch(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "this_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prediction = ntm:forward(this_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prediction = ntm:forward({torch.LongTensor{{1},{2}}, torch.LongTensor{{1,2},{3,4}}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "torch.LongTensor{{1},{2}}:dim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test = ld_1:forward(torch.LongTensor{{1,2},{2,3}, {4,6}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test2 = nn.Reshape(2,40,true):forward(test:type('torch.DoubleTensor'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn.SplitTable(2,3):forward(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "this_g = le_start:forward(this_batch[1])\n",
    "this_lef = nn.Reshape(le_in:size()[2], true):forward(this_g:type('torch.DoubleTensor'))\n",
    "this_lt1 = lt1:forward(this_lef)\n",
    "this_lt = nn.Sigmoid():forward(this_lt1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "this_ld1 = ld_1:forward(this_batch[2]) \n",
    "this_ldb = nn.SoftMax():forward(this_ld1)\n",
    "this_ldf = nn.Reshape(2,pre_w1:size()[2],true):forward(this_ldb)\n",
    "this_lds = nn.SplitTable(2,3):forward(this_ldf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn.MM(false,true):forward({torch.DoubleTensor{{1,2,3},{7,8,9}},torch.DoubleTensor{{3,4,5}, {6,7,8}}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input1 = torch.DoubleTensor{{1,2,3},{7,8,9}}\n",
    "input2 = torch.DoubleTensor{{3,4,5}, {6,7,8}}\n",
    "buffer = input1:new()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "buffer:cmul(input1, input2):sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "buffer = this_lt:new()\n",
    "bob = buffer:cmul(this_lt, this_lds[1]):sum(2) \n",
    "print(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_lt = gram_stack:forward(this_batch[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_ld = doc_stack:forward(this_batch[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nn.DotProduct():forward({test_lt, test_ld[1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = nn.Identity()()\n",
    "din = nn.Identity()()\n",
    "-- the gram stack\n",
    "le_start = nn.Dictionary(le_in:size()[1], le_in:size()[2])\n",
    "le_start.weight = le_in\n",
    "le = le_start({g}) \n",
    "-- mark this not-trainable\n",
    "function le:updateParameters(learningRate)\n",
    "end\n",
    "le_flat = nn.Reshape(le_in:size()[2], true)(le)\n",
    "\n",
    "lt1 = nn.Linear(pre_w2:size()[1], pre_w2:size()[2])\n",
    "lt1.weight = pre_w2:transpose(1,2)\n",
    "lt1.bias = torch.zeros(pre_w2:size()[2])\n",
    "-- No bias in the lt module\n",
    "function lt1:accUpdateGradParameters(input, gradOutput, lr)\n",
    "   local gradWeight = self.gradWeight\n",
    "   self.gradWeight = self.weight\n",
    "   self:accGradParameters(input, gradOutput, -lr)\n",
    "   self.gradWeight = gradWeight\n",
    "end\n",
    "\n",
    "function lt1:accGradParameters(input, gradOutput, scale)\n",
    "   scale = scale or 1\n",
    "   if input:dim() == 1 then\n",
    "      self.gradWeight:addr(scale, gradOutput, input)\n",
    "   elseif input:dim() == 2 then\n",
    "      self.gradWeight:addmm(scale, gradOutput:t(), input)\n",
    "   end\n",
    "end\n",
    "lt2 = lt1({le_flat})\n",
    "lt = nn.Sigmoid()({lt2})\n",
    "\n",
    "-- the document stack\n",
    "ld_1 = nn.Dictionary(pre_w1:size()[1], pre_w1:size()[2])\n",
    "ld_1.weight = pre_w1\n",
    "ld_2 = ld_1({din})\n",
    "ld_both = nn.SoftMax()(ld_2)\n",
    "\n",
    "-- the scoring stack\n",
    "ld_flat = nn.Reshape(2,pre_w1:size()[2],true)({ld_both})\n",
    "ld_split = nn.SplitTable(2,3)(ld_flat)\n",
    "ld_pos, ld_neg = ld_split:split(2)\n",
    "ls_pos = nn.DotProduct()({lt, ld_pos})\n",
    "ls_neg = nn.DotProduct()({lt, ld_neg})\n",
    "\n",
    "ntm = nn.gModule({g, din}, {ls_pos, ls_neg})\n",
    "\n",
    "loss_out = nn.L1HingeEmbeddingCriterion(0.5):cuda()"
   ]
  }
 ],
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 },
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