RNN 6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = require 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dir = '/home/arun/data/seqsimple2/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X,Y = data.loadXY(dir)\n",
    "data.convert(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "function repl(N)\n",
    "    local net = nn.ConcatTable()\n",
    "    for i=1,N do \n",
    "        net:add(nn.Identity())\n",
    "    end\n",
    "    return net\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require 'nn'\n",
    "require 'rnn'\n",
    "net = nn.Sequential()\n",
    "net:add(nn.Reshape(1,50,200))\n",
    "net:add(nn.SpatialConvolution(1,64,3,3,1,1,1,1))\n",
    "net:add(nn.SpatialBatchNormalization(64,1e-3))\n",
    "net:add(nn.ReLU(true))\n",
    "net:add(nn.SpatialMaxPooling(2,2,2,2))\n",
    "net:add(nn.SpatialConvolution(64,64,3,3,1,1,1,1))\n",
    "net:add(nn.SpatialBatchNormalization(64,1e-3))\n",
    "net:add(nn.ReLU(true))\n",
    "net:add(nn.SpatialMaxPooling(2,2,2,2))\n",
    "net:add(nn.SpatialConvolution(64,64,3,3,1,1,1,1))\n",
    "net:add(nn.SpatialBatchNormalization(64,1e-3))\n",
    "net:add(nn.ReLU(true))\n",
    "net:add(nn.SpatialMaxPooling(2,2,2,2))\n",
    "net:add(nn.SpatialConvolution(64,64,3,3,1,1,1,1))\n",
    "net:add(nn.SpatialBatchNormalization(64,1e-3))\n",
    "net:add(nn.ReLU(true))\n",
    "net:add(nn.SpatialMaxPooling(2,2,2,2))\n",
    "net:add(nn.SpatialConvolution(64,64,3,3,1,1,1,1))\n",
    "net:add(nn.SpatialBatchNormalization(64,1e-3))\n",
    "net:add(nn.ReLU(true))\n",
    "net:add(nn.SpatialMaxPooling(2,2,2,2))\n",
    "--net:add(nn.View(64*1*6))\n",
    "net:add(nn.Reshape(8,48))\n",
    "net:add(nn.SplitTable(2,3))\n",
    "enc = nn.Sequential()\n",
    "enc:add(net)\n",
    "enc:add(nn.Sequencer(nn.LSTM(48, 48)))\n",
    "enc:add(nn.SelectTable(-1))\n",
    "dec = nn.Sequential()\n",
    "dec:add(enc)\n",
    "dec:add(repl(7))\n",
    "\n",
    "mlp = nn.Sequential()\n",
    "       :add(nn.LSTM(48, 48))\n",
    "       :add(nn.Linear(48, 36))\n",
    "       :add(nn.LogSoftMax())\n",
    "\n",
    "dec:add(nn.Sequencer(mlp))\n",
    "--net:add(repl(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require 'cunn';\n",
    "require 'nn';\n",
    "require 'rnn';\n",
    "dec = torch.load('seq88.t7')\n",
    "dec = dec:cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "criterion = nn.SequencerCriterion(nn.ClassNLLCriterion())\n",
    "criterion = criterion:cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rutil = require 'rutil'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Xt,Yt,Xv,Yv = data.split(X,Y,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tnet = nn.SplitTable(2,2):cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.6\t{\n",
       "  1 : 99.3\n",
       "  2 : 94.6\n",
       "  3 : 91.9\n",
       "  4 : 93.1\n",
       "  5 : 93.4\n",
       "  6 : 97.4\n",
       "  7 : 99.5\n",
       "}\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rutil.valid(dec,Xv,Yv,512,tnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "function rutil.kfacc(outputs,targets)\n",
    "    local Y,y = nil,nil;\n",
    "    local N = outputs[1]:size(1)\n",
    "    local C = outputs[1]:size(2)\n",
    "    for k=1,#outputs do \n",
    "        Y = Y and torch.cat(Y,outputs[k]:reshape(N,1,C),2) or outputs[k]:reshape(N,1,C)\n",
    "        y = y and torch.cat(y,targets[k]:reshape(N,1),2) or targets[k]:reshape(N,1)\n",
    "    end\n",
    "    local t,idx = Y:max(3)\n",
    "    return idx:squeeze():eq(y):sum(2):eq(#outputs):sum()\n",
    "end\n",
    "\n",
    "function rutil.kvalid(rnn,Xv,Yv,batchSize,tnet)\n",
    "    local batchSize = batchSize or 16\n",
    "    local acc = 0\n",
    "    local acci = {}\n",
    "    local Nv = Xv:size(1)\n",
    "    rnn:evaluate()\n",
    "    for i=1,Nv,batchSize do \n",
    "        xlua.progress(i/batchSize, Nv/batchSize)\n",
    "        local j = math.min(Nv,i+batchSize-1)\n",
    "        local Xb = Xv[{{i,j}}]:cuda()            \n",
    "        local Yb = Yv[{{i,j}}]:cuda()\n",
    "        local inputs = Xb\n",
    "        local targets = tnet:forward(Yb)\n",
    "        local outputs = rnn:forward(inputs)\n",
    "        local aa,ai = rutil.kfacc2(outputs,targets)\n",
    "        acc = acc + aa\n",
    "        rnn:forget()\n",
    "    end\n",
    "    return (acc*100)/Nv,acci\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
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       "loss\t"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "56.62282755971\t\n",
       "train\t97.745812807882\t\n"
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     "execution_count": 16,
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     "data": {
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       "v\t97.528571428571\tmaxv\t97.528571428571\t\n",
       "{\n",
       "  1 : 99.6\n",
       "  2 : 97.3\n",
       "  3 : 94.7\n",
       "  4 : 95.6\n",
       "  5 : 97.1\n",
       "  6 : 98.8\n",
       "  7 : 99.6\n",
       "}\n",
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       "loss\t54.96096020937\t\n",
       "train\t97.913300492611\t\n"
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     "execution_count": 16,
     "metadata": {},
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       "v\t97.514285714286\tmaxv\t97.528571428571\t\n",
       "{\n",
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       "  2 : 97.2\n",
       "  3 : 94.8\n",
       "  4 : 95.8\n",
       "  5 : 96.9\n",
       "  6 : 98.7\n",
       "  7 : 99.6\n",
       "}\n",
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       "\n"
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       "loss\t53.630336015352\t\n",
       "train\t98.059113300493\t\n"
      ]
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     "metadata": {},
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     "data": {
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       "v\t97.485714285714\tmaxv\t97.528571428571\t\n",
       "{\n",
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       "  2 : 97.1\n",
       "  3 : 95\n",
       "  4 : 95.7\n",
       "  5 : 96.8\n",
       "  6 : 98.6\n",
       "  7 : 99.6\n",
       "}\n",
       "11:34:19  IST\t\n",
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     "data": {
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       "loss\t52.573484056762\t\n",
       "train\t98.160591133005\t\n"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "v\t97.471428571429\tmaxv\t97.528571428571\t\n",
       "{\n",
       "  1 : 99.6\n",
       "  2 : 97.2\n",
       "  3 : 94.7\n",
       "  4 : 95.8\n",
       "  5 : 96.8\n",
       "  6 : 98.6\n",
       "  7 : 99.6\n",
       "}\n",
       "11:36:51  IST\t\n",
       "11:36:51  IST\t\n",
       "5\t8\t\n"
      ]
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    },
    {
     "data": {
      "text/plain": [
       "Progress: 272.015625 / 453.125\t\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Progress: 317.015625 / 453.125\t\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Progress: 363.015625 / 453.125\t\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Progress: 408.015625 / 453.125\t\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "loss\t72.135594595756\t\n",
       "train\t96.106896551724\t\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Progress: 0.0625 / 62.5\t\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "v\t88.285714285714\tmaxv\t88.285714285714\t\n",
       "{\n",
       "  1 : 98\n",
       "  2 : 86.3\n",
       "  3 : 80.2\n",
       "  4 : 80.6\n",
       "  5 : 84.1\n",
       "  6 : 91.1\n",
       "  7 : 97.7\n",
       "}\n",
       "12:37:25  IST\t\n",
       "88.285714285714\t\n",
       "\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rutil.train(dec,criterion,Xt,Yt,Xv,Yv,1,64,tnet,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "function rutil.check(y,Y)\n",
    "    --print(y,Y)\n",
    "    for i=1,y:nElement() do\n",
    "        if(Y[i]==1) then return true end\n",
    "        if(y[i]~=Y[i]) then return false end\n",
    "    end\n",
    "    return true\n",
    "end\n",
    "function rutil.kfacc2(outputs,targets)\n",
    "    local Y,y = nil,nil;\n",
    "    local N = outputs[1]:size(1)\n",
    "    local C = outputs[1]:size(2)\n",
    "    for k=1,#outputs do \n",
    "        Y = Y and torch.cat(Y,outputs[k]:reshape(N,1,C),2) or outputs[k]:reshape(N,1,C)\n",
    "        y = y and torch.cat(y,targets[k]:reshape(N,1),2) or targets[k]:reshape(N,1)\n",
    "    end\n",
    "    local t,idx = Y:max(3)\n",
    "    idx = idx:squeeze()\n",
    "    local acc = 0\n",
    "    for i=1,y:size(1) do\n",
    "        if(rutil.check(idx[i],y[i])) then \n",
    "            acc = acc + 1\n",
    "            --print('true')\n",
    "        else\n",
    "            --print('false')\n",
    "        end\n",
    "    end\n",
    "    return acc\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.1\t{\n",
       "}\n"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rutil.kvalid(dec,Xv,Yv,64,tnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.save('seq44.t7',dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "iTorch",
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  "language_info": {
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   "version": "20100"
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