proto_sigmoid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torch.autograd as autograd\n",
    "import torchvision.transforms as transforms\n",
    "from torch.autograd import Variable\n",
    "from torch.nn.parameter import Parameter\n",
    "%matplotlib inline\n",
    "import pylab as pl\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Block(nn.Module):\n",
    "    def __init__(self, insize, outsize):\n",
    "        super(Block, self).__init__()\n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Conv2d(insize, outsize, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(outsize),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2)\n",
    "        )\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        return self.layers(inp)\n",
    "\n",
    "class ENCODER(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ENCODER, self).__init__()\n",
    "        self.block1 = Block(3,64)\n",
    "        self.block2 = Block(64,64)\n",
    "        self.block3 = Block(64,64)\n",
    "        self.block4 = Block(64,64)\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        out = self.block1(inp)\n",
    "        out = self.block2(out)\n",
    "        out = self.block3(out)\n",
    "        out = self.block4(out)\n",
    "        return out.view(out.size(0),-1)\n",
    "\n",
    "class smartPREDICTOR(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(smartPREDICTOR, self).__init__()\n",
    "        self.total = 0\n",
    "        self.way = 0\n",
    "        self.preds = None\n",
    "        self.sm = nn.LogSoftmax()\n",
    "        \n",
    "    def initialize(self, way):\n",
    "        self.way = way\n",
    "        self.preds = nn.Linear(1600,self.way).cuda()\n",
    "        \n",
    "    def forward(self, inp):\n",
    "        newinp = inp.view(inp.size(0),-1)\n",
    "        preds = self.preds(newinp)\n",
    "        probs = self.sm(preds)\n",
    "        return probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset = torch.load('mIN_train.pth')\n",
    "testset = torch.load('mIN_test.pth')\n",
    "valset = torch.load('mIN_val.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "standardize = transforms.Compose([\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize(mean=[.485,.456,.406],std=[.229,.224,.225])\n",
    "            ])\n",
    "alter = transforms.Compose([\n",
    "            transforms.ToPILImage(),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            transforms.RandomCrop(84,padding=10),\n",
    "            standardize\n",
    "            ])\n",
    "\n",
    "def batchmaker(way,trainshot,testshot,theset,alterful=False):\n",
    "    classes = np.random.choice(len(theset),way)\n",
    "    if alterful:\n",
    "        li = [torch.cat([alter(theset[cl][i]).view(1,3,84,84) for i in \n",
    "                         np.random.choice(600,trainshot+testshot)],dim=0).float()\n",
    "              for cl in classes]\n",
    "    else:\n",
    "        li = [torch.cat([standardize(theset[cl][i]).view(1,3,84,84) for i in \n",
    "                         np.random.choice(600,trainshot+testshot)],dim=0).float()\n",
    "              for cl in classes]\n",
    "    support = torch.cat([t[:trainshot,:,:,:] for t in li],dim=0)\n",
    "    stargs = torch.LongTensor([i//trainshot for i in range(trainshot*way)])\n",
    "    query = torch.cat([t[trainshot:,:,:,:] for t in li],dim=0)\n",
    "    qtargs = torch.LongTensor([i//testshot for i in range(testshot*way)])\n",
    "#     print(support.size(),query.size())\n",
    "    return(Variable(support).cuda(),\n",
    "           Variable(query, volatile=(not alterful)).cuda(),\n",
    "           Variable(qtargs, volatile=(not alterful)).cuda(),\n",
    "           Variable(stargs).cuda()\n",
    "          )\n",
    "    \n",
    "def newevaluate(embed,model,criterion,way,trainshot,testshot,theset):\n",
    "    losstracker = 0\n",
    "    acctracker = 0\n",
    "    acclist = []\n",
    "    for i in range(reps):\n",
    "#         print(i)\n",
    "        support, query, qtargs, stargs = batchmaker(way,trainshot,testshot,theset)\n",
    "        embeds = embed(support)\n",
    "        model.initialize(way)\n",
    "        optimizer = optim.Adam(model.parameters(),lr=.01)\n",
    "        for i in range(patience):\n",
    "            model.zero_grad()\n",
    "            preds = model(embeds.detach())\n",
    "            loss = criterion(preds, stargs)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "    #     print(newmodel.weights.data)\n",
    "        qembeds = embed(query)\n",
    "        preds = model(qembeds)\n",
    "\n",
    "        # Val loss\n",
    "        loss = criterion(preds,qtargs)\n",
    "        losstracker += loss.data[0]\n",
    "        \n",
    "        # Val accuracy\n",
    "        _,bins = torch.max(preds,1)\n",
    "        acc = torch.sum(torch.eq(bins,qtargs)).data[0]/testshot/way\n",
    "        acctracker += acc\n",
    "        acclist.append(acc)\n",
    "#         display.clear_output(wait=False)\n",
    "        \n",
    "    return losstracker/reps,acctracker/reps,np.var(acclist)\n",
    "\n",
    "way = 20\n",
    "evalway = 5\n",
    "trainshot = 30\n",
    "testshot = 5\n",
    "patience = 15\n",
    "reps = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113088  parameters in neural net.\n"
     ]
    }
   ],
   "source": [
    "torch.cuda.set_device(0)\n",
    "embed = ENCODER().cuda()\n",
    "model = smartPREDICTOR().cuda()\n",
    "losstracker,evallosstracker,evalacctracker = [],[],[]\n",
    "nweights = sum([i.numel() for i in list(embed.parameters())])\n",
    "print(nweights,\" parameters in neural net.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JBnpJK2sPtJmTcUNxNeOGJjIizRt32j582LWv/ogKCG0r874zOyvr2lxIOh50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\nIiIiIuLH/j9GrATAdaPaogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9442b65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss is: 1.289160881280899\n",
      "Validation accuracy is: 0.7144000000000001\n",
      "Validation loss is: 0.7248118019104004\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# TRAIN!\n",
    "criterion = nn.NLLLoss().cuda()\n",
    "vbity = 250\n",
    "epoch = 2000\n",
    "\n",
    "runningloss = 0\n",
    "te_optimizer = None\n",
    "tr_optimizer = None\n",
    "for it in range(1):\n",
    "    if it%10==0:\n",
    "        print(it)\n",
    "    \n",
    "    # Build batch\n",
    "    support, query, qtargs, stargs = batchmaker(way,trainshot,testshot,trainset,alterful=True)\n",
    "    \n",
    "    # Predict\n",
    "    embed.zero_grad()\n",
    "    embeds = embed(support)\n",
    "    model.initialize(way)\n",
    "    tr_optimizer = optim.Adam(model.parameters(),lr=.01)\n",
    "    for i in range(patience):\n",
    "        model.zero_grad()\n",
    "        preds = model(embeds.detach())\n",
    "        loss = criterion(preds, stargs)\n",
    "        loss.backward()\n",
    "        tr_optimizer.step()\n",
    "#     print(model.weights.data)\n",
    "    qembeds = embed(query)\n",
    "    preds = model(qembeds)\n",
    "\n",
    "    # Calculate Loss\n",
    "    loss = criterion(preds, qtargs)\n",
    "    runningloss += loss.data[0]\n",
    "    \n",
    "    # Backprop\n",
    "    if it%epoch == 0:\n",
    "        te_optimizer = optim.Adam(embed.parameters(),lr=.001/(2**(it//epoch)))\n",
    "    loss.backward()\n",
    "#     nn.utils.clip_grad_norm(model.parameters(), 1)\n",
    "    te_optimizer.step()\n",
    "    \n",
    "    # Report\n",
    "    if it%vbity == vbity-1:\n",
    "        display.clear_output(wait=True)\n",
    "\n",
    "        losstracker.append(runningloss/vbity)\n",
    "        evalloss, evalacc, _ = newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset)\n",
    "        evallosstracker.append(evalloss)\n",
    "        evalacctracker.append(evalacc)\n",
    "        \n",
    "        pl.figure(1,figsize=(15,5))\n",
    "        pl.subplot(1,2,1)\n",
    "        pl.plot(losstracker)\n",
    "        pl.plot(evallosstracker)\n",
    "        pl.ylim((0,4))\n",
    "        pl.title(\"Loss: Training Blue, Validation Gold\")\n",
    "        pl.subplot(1,2,2)\n",
    "        pl.plot(evalacctracker[::-1])\n",
    "        pl.ylim((0,1))\n",
    "        pl.title(\"Validation Acc\")\n",
    "        pl.show()\n",
    "        \n",
    "        print(\"Train loss is: \"+str(runningloss/vbity)+\n",
    "              \"\\nValidation accuracy is: \"+str(evalacc)+\n",
    "              \"\\nValidation loss is: \"+str(evalloss)+\"\\n\")\n",
    "        runningloss = 0\n",
    "\n",
    "# META\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dww78/anaconda3/lib/python3.6/site-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type ENCODER. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/home/dww78/anaconda3/lib/python3.6/site-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type Block. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n",
      "/home/dww78/anaconda3/lib/python3.6/site-packages/torch/serialization.py:147: UserWarning: Couldn't retrieve source code for container of type smartPREDICTOR. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "name = 'sigmoid_highshot'\n",
    "torch.save((embed,model,losstracker,evallosstracker,evalacctracker),'saved_models/'+name+'.pth')\n",
    "torch.save(embed.cpu().state_dict(),'saved_models/'+name+'_cpu.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7235450000000001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(evalacctracker[-1000:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class PREDICTOR(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(PREDICTOR, self).__init__()\n",
    "        self.sm = nn.LogSoftmax()\n",
    "        \n",
    "    def forward(self, support, inp, way):\n",
    "        assert support.size(0)%way == 0,\"Error: classes are not the same size!\"\n",
    "        support = support.view(way,support.size(0)//way,-1)\n",
    "        centroids = torch.mean(support, 1).view(support.size(0),-1)\n",
    "#         temp1 = torch.stack([centroids]*inp.size(0))\n",
    "#         temp2 = torch.stack([inp]*centroids.size(0)).transpose(0,1)\n",
    "#         print(temp1.size(),temp2.size())\n",
    "        distmat = torch.sum((torch.stack([centroids]*inp.size(0))-torch.stack([inp]*centroids.size(0))\n",
    "                             .transpose(0,1))**2,2).squeeze()*-1\n",
    "        probs = self.sm(distmat)\n",
    "        return probs\n",
    "embed, _ = torch.load('saved_models/naive_2_noinfo.pth')\n",
    "embed = embed.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N0wr41NB+OrUyBqncRbqZvYfreGLVTp54eyf7jtRzTv+e3DCtgM9NzCE5QR+v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\n6P8DsncXy0YbHmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83b89d9a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build batch\n",
    "# embed, model, _,_,_ = torch.load('saved_models/smart_1.pth')\n",
    "support, query, qtargs, stargs = batchmaker(way,trainshot,testshot,valset,alterful=False)\n",
    "criterion = nn.NLLLoss().cuda()\n",
    "embed = embed.cuda()\n",
    "model = model.cuda()\n",
    "\n",
    "# Predict\n",
    "tracker = []\n",
    "embed.zero_grad()\n",
    "embeds = embed(support)\n",
    "model.initialize(way)\n",
    "tr_optimizer = optim.Adam(model.parameters(),lr=.01)\n",
    "for i in range(15):\n",
    "    model.zero_grad()\n",
    "    preds = model(embeds.detach())\n",
    "    loss = criterion(preds, stargs)\n",
    "    tracker.append(loss.data[0])\n",
    "    loss.backward()\n",
    "    tr_optimizer.step()\n",
    "# print(model.weights.data)\n",
    "# print(torch.var(model.weights.data.view(-1)),torch.max(model.weights.data),torch.min(model.weights.data))\n",
    "pl.figure()\n",
    "pl.plot(tracker)\n",
    "pl.ylim((0,4))\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 20.6023\n",
       "[torch.cuda.FloatTensor of size 1 (GPU 0)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mean(torch.norm(embeds,2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# BASELINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.91514739215374, 0.6385333333333335, 0.010876071111111113)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.6446040686964989, 0.7650666666666668, 0.0088703288888888881)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,trainset))\n",
    "# TRAINSET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.7136507552862167, 0.7288000000000002, 0.012978559999999997)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET, 100 PATIENCE, NAIVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.726845205873251, 0.7353333333333336, 0.0094271111111111101)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET, 100 PATIENCE, SMART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# EXPERIMENTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9770350706577301, 0.6170666666666665, 0.010962062222222221)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET, 100 PATIENCE, SMART. LR .1, MOM 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9578658026456833, 0.6214000000000002, 0.013506039999999999)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET, 100 PATIENCE, NAIVE. LR .1, MOM 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9450288346409798, 0.6406666666666669, 0.013203111111111108)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET, 50 PATIENCE, SMART. LR .1, MOM 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9806817117333412, 0.6148000000000002, 0.011196071111111108)\n"
     ]
    }
   ],
   "source": [
    "print(newevaluate(embed,model,criterion,evalway,trainshot,testshot,testset))\n",
    "# TESTSET, 20 PATIENCE, SMART. LR 1, MOM .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ajbb+3CbioE+mB+dNLPY65wmSZ3QIGgAuJuNB9PqdvVfY/5tE9Hdk++8Q0W+8x+N48+bt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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f184c4b0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import randint\n",
    "pl.figure()\n",
    "pl.imshow(testset[randint(0,len(testset)-1)][randint(0,len(testset[0])-1)])\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ZgUvTVsZ/P2P9rCmuMSxdlsuC9fn0K1N0+D2QJyFxaUsWLeTAzq1qDyIBKk0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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f79e26b8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss is: 1.4755005943775177\n",
      "Validation accuracy is: 0.6951111111111113\n",
      "Validation loss is: 0.7553736646970113\n",
      "\n",
      "188500\n",
      "188510\n",
      "188520\n",
      "188530\n",
      "188540\n",
      "188550\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-186fa9537132>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m# Build batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0msupport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatchmaker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mway\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrainshot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestshot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrainset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malterful\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;31m# Predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-84462d890901>\u001b[0m in \u001b[0;36mbatchmaker\u001b[0;34m(way, trainshot, testshot, theset, alterful)\u001b[0m\n\u001b[1;32m     15\u001b[0m         li = [torch.cat([alter(trainset[cl][i]).view(1,3,84,84) for i in \n\u001b[1;32m     16\u001b[0m                          np.random.choice(600,trainshot+testshot)],dim=0).float()\n\u001b[0;32m---> 17\u001b[0;31m               for cl in classes]\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         li = [torch.cat([standardize(trainset[cl][i]).view(1,3,84,84) for i in \n",
      "\u001b[0;32m<ipython-input-4-84462d890901>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     15\u001b[0m         li = [torch.cat([alter(trainset[cl][i]).view(1,3,84,84) for i in \n\u001b[1;32m     16\u001b[0m                          np.random.choice(600,trainshot+testshot)],dim=0).float()\n\u001b[0;32m---> 17\u001b[0;31m               for cl in classes]\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         li = [torch.cat([standardize(trainset[cl][i]).view(1,3,84,84) for i in \n",
      "\u001b[0;32m<ipython-input-4-84462d890901>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mway\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0malterful\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         li = [torch.cat([alter(trainset[cl][i]).view(1,3,84,84) for i in \n\u001b[0m\u001b[1;32m     16\u001b[0m                          np.random.choice(600,trainshot+testshot)],dim=0).float()\n\u001b[1;32m     17\u001b[0m               for cl in classes]\n",
      "\u001b[0;32m/home/dww78/anaconda3/lib/python3.6/site-packages/torchvision-0.1.9-py3.6.egg/torchvision/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dww78/anaconda3/lib/python3.6/site-packages/torchvision-0.1.9-py3.6.egg/torchvision/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransforms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/dww78/anaconda3/lib/python3.6/site-packages/torchvision-0.1.9-py3.6.egg/torchvision/transforms.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, tensor)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;31m# TODO: make efficient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m             \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiv_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# TRAIN!\n",
    "criterion = nn.NLLLoss().cuda()\n",
    "vbity = 100\n",
    "epoch = 2000\n",
    "\n",
    "losstracker,evallosstracker,evalacctracker = [],[],[]\n",
    "runningloss = 0\n",
    "optimizer = None\n",
    "for it in range(epoch*100):\n",
    "    if it%10==0:\n",
    "        print(it)\n",
    "    \n",
    "    # Build batch\n",
    "    support, query, qtargs, _ = batchmaker(way,trainshot,testshot,trainset,alterful=True)\n",
    "    \n",
    "    # Predict\n",
    "    embed.zero_grad()\n",
    "    model.zero_grad()\n",
    "    embeds = embed(support)\n",
    "    qembeds = embed(query)\n",
    "    preds = model(embeds,qembeds,way)\n",
    "\n",
    "    # Calculate Loss\n",
    "    loss = criterion(preds, targs)\n",
    "    runningloss += loss.data[0]\n",
    "    \n",
    "    # Backprop\n",
    "    if it%epoch == 0:\n",
    "        optimizer = optim.Adam(embed.parameters(),lr=.001/(2**(it//epoch)))\n",
    "    loss.backward()\n",
    "#     nn.utils.clip_grad_norm(model.parameters(), 1)\n",
    "    optimizer.step()\n",
    "    \n",
    "    # Report\n",
    "    if it%vbity == vbity-1:\n",
    "        display.clear_output(wait=True)\n",
    "\n",
    "        losstracker.append(runningloss/vbity)\n",
    "        evalloss, evalacc = evaluate(embed,model,criterion,evalway,trainshot,testshot)\n",
    "        evallosstracker.append(evalloss)\n",
    "        evalacctracker.append(evalacc)\n",
    "        \n",
    "        pl.figure(1,figsize=(15,5))\n",
    "        pl.subplot(1,2,1)\n",
    "        pl.plot(losstracker)\n",
    "        pl.plot(evallosstracker)\n",
    "        pl.ylim((0,4))\n",
    "        pl.title(\"Loss: Training Blue, Validation Gold\")\n",
    "        pl.subplot(1,2,2)\n",
    "        pl.plot(evalacctracker)\n",
    "        pl.ylim((0,1))\n",
    "        pl.title(\"Validation Acc\")\n",
    "        pl.show()\n",
    "        \n",
    "        print(\"Train loss is: \"+str(runningloss/vbity)+\n",
    "              \"\\nValidation accuracy is: \"+str(evalacc)+\n",
    "              \"\\nValidation loss is: \"+str(evalloss)+\"\\n\")\n",
    "        runningloss = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Paper model achieves (over last 1000):\n",
    "    0.71156711111111126\n",
    "after 188500 iters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "-6.7075e-03 -5.0079e+00 -2.7911e+01\n",
       "-2.9057e+00 -5.6263e-02 -4.5704e+01\n",
       "-1.8272e+01 -4.3870e+01 -1.1600e-08\n",
       "[torch.FloatTensor of size 3x3]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "support = supportmaker(nclass,shot)\n",
    "inp = inpmaker(nclass)\n",
    "embeds = embed(support)\n",
    "query = embed(inp)\n",
    "model(embeds,query)"
   ]
  }
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