Pytorch example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pytorch example\n",
    "Taken from https://github.com/rguthrie3/DeepLearningForNLPInPytorch/blob/master/Deep%20Learning%20for%20Natural%20Language%20Processing%20with%20Pytorch.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "torch.manual_seed(1)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"poster\")\n",
    "sns.set_style(\"ticks\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.39670073,  0.10558598,  0.81686217,  0.37422602,  0.29857204,\n",
       "        0.33278065,  0.66014766,  0.55107125,  0.2067656 ,  0.48672191])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.rand(10)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0.3967\n",
       " 0.1056\n",
       " 0.8169\n",
       " 0.3742\n",
       " 0.2986\n",
       " 0.3328\n",
       " 0.6601\n",
       " 0.5511\n",
       " 0.2068\n",
       " 0.4867\n",
       "[torch.FloatTensor of size 10]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_tch = torch.Tensor(a)\n",
    "a_tch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39670073986053467"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_tch[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "(0 ,.,.) = \n",
       "  0.4170  0.9972  0.7203  0.9326\n",
       "  0.0001  0.1281  0.3023  0.9990\n",
       "  0.1468  0.2361  0.0923  0.3966\n",
       "\n",
       "(1 ,.,.) = \n",
       "  0.1863  0.3879  0.3456  0.6697\n",
       "  0.3968  0.9355  0.5388  0.8463\n",
       "  0.4192  0.3133  0.6852  0.5245\n",
       "[torch.FloatTensor of size 2x3x4]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_tch = torch.rand((2,3,4))\n",
    "a_tch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 1  5\n",
       " 7  6\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.Tensor([[0, 1.], [2, 3]])\n",
    "y = torch.Tensor([[1, 4.], [5, 3]])\n",
    "x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0  1  1  4\n",
       " 2  3  5  3\n",
       "[torch.FloatTensor of size 2x4]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cat([x,y], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 0  1  2  3\n",
       "[torch.FloatTensor of size 1x4]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.view((1,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autograd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 1\n",
      " 2\n",
      "[torch.FloatTensor of size 2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = Variable(torch.Tensor([1,2]), requires_grad=True)\n",
    "print(x.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1\n",
       " 2\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 4\n",
       " 7\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = Variable(torch.Tensor([3,5]), requires_grad=True)\n",
    "z = x+y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.autograd._functions.basic_ops.Add at 0x7f127c87a3c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.creator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.autograd._functions.reduce.Sum at 0x7f127c87a588>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = z.sum()\n",
    "s.creator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      " 1\n",
      " 1\n",
      "[torch.FloatTensor of size 2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "s.backward()\n",
    "print(x.grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<torch.autograd._functions.basic_ops.Add object at 0x7f121a662128>\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "## Tensors are data objects\n",
    "x = torch.rand((2,2))\n",
    "y = torch.rand((2,2))\n",
    "z = x+y\n",
    "\n",
    "## Variables are variable objects also contain the grad details and computational graph details\n",
    "var_x = Variable(x)\n",
    "var_y = Variable(y)\n",
    "var_z = var_x + var_y\n",
    "print(var_z.creator)\n",
    "\n",
    "## I am breaking the chain of the variable by creating a new variable with same data. \n",
    "## Gradients will not propogate\n",
    "var_z_data = var_z.data\n",
    "new_var_z = Variable(var_z_data)\n",
    "print(new_var_z.creator)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_x.data.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "-0.9596\n",
       "[torch.FloatTensor of size 1]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.randn(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       " 2.1934 -0.4928\n",
       "-0.9578  0.7760\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.randn([1,2]).expand_as(torch.randn((2,2))) + torch.randn((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 0.2045  0.8781\n",
       " 0.4435  0.2296\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_x.transpose(1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 0.0000  0.5331  0.0000  0.0000\n",
       " 0.0000  0.7109  0.0000  0.2759\n",
       "[torch.FloatTensor of size 2x4]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = nn.Linear(x.size(1), 4)\n",
    "h1 = l1(var_x)\n",
    "F.relu(h1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 0.1062  0.4435  0.1901  0.2601\n",
       " 0.0826  0.4353  0.2002  0.2818\n",
       "[torch.FloatTensor of size 2x4]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.softmax(h1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1\n",
       " 1\n",
       "[torch.FloatTensor of size 2x1]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.softmax(h1).sum(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Language prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [ (\"me gusta comer en la cafeteria\".split(), \"SPANISH\"),\n",
    "         (\"Give it to me\".split(), \"ENGLISH\"),\n",
    "         (\"No creo que sea una buena idea\".split(), \"SPANISH\"),\n",
    "         (\"No it is not a good idea to get lost at sea\".split(), \"ENGLISH\") ]\n",
    "\n",
    "test_data = [ (\"Yo creo que si\".split(), \"SPANISH\"),\n",
    "              (\"it is lost on me\".split(), \"ENGLISH\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Vocab(object):\n",
    "    def __init__(self, name=\"vocab\",\n",
    "                 offset_items=tuple([]),\n",
    "                 UNK=None):\n",
    "        self.name = name\n",
    "        self.item2idx = {}\n",
    "        self.idx2item = []\n",
    "        self.size = 0\n",
    "        self.UNK = UNK\n",
    "        \n",
    "        self.batch_add(offset_items)\n",
    "        if UNK is not None:\n",
    "            self.add(UNK)\n",
    "            self.UNK_ID = self.item2idx[self.UNK]\n",
    "        self.offset = self.size\n",
    "        \n",
    "    def add(self, item):\n",
    "        if item not in self.item2idx:\n",
    "            self.item2idx[item] = self.size\n",
    "            self.size += 1\n",
    "            self.idx2item.append(item)\n",
    "            \n",
    "    def batch_add(self, items):\n",
    "        for item in items:\n",
    "            self.add(item)\n",
    "            \n",
    "    def getidx(self, item):\n",
    "        if item not in self.item2idx:\n",
    "            if self.UNK is None:\n",
    "                raise RuntimeError(\"UNK is not defined. %s not in vocab.\" % item)\n",
    "            return self.UNK_ID\n",
    "        return self.item2idx[item]\n",
    "            \n",
    "    def __repr__(self):\n",
    "        return \"Vocab(name={}, size={:d}, UNK={}, offset={:d})\".format(\n",
    "            self.name, self.size,\n",
    "            self.UNK, self.offset\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocab(name=words, size=24, UNK=UNK, offset=1) ['UNK', 'me', 'gusta', 'comer', 'en', 'la', 'cafeteria', 'Give', 'it', 'to', 'No', 'creo', 'que', 'sea', 'una', 'buena', 'idea', 'is', 'not', 'a', 'good', 'get', 'lost', 'at'] {'is': 17, 'gusta': 2, 'Give': 7, 'No': 10, 'lost': 22, 'me': 1, 'a': 19, 'una': 14, 'get': 21, 'cafeteria': 6, 'sea': 13, 'idea': 16, 'en': 4, 'good': 20, 'not': 18, 'comer': 3, 'at': 23, 'buena': 15, 'que': 12, 'UNK': 0, 'to': 9, 'it': 8, 'creo': 11, 'la': 5}\n",
      "Vocab(name=labels, size=2, UNK=None, offset=0) ['SPANISH', 'ENGLISH'] {'SPANISH': 0, 'ENGLISH': 1}\n"
     ]
    }
   ],
   "source": [
    "word_vocab = Vocab(\"words\", UNK=\"UNK\")\n",
    "label_vocab = Vocab(\"labels\")\n",
    "\n",
    "for sent, label in data:\n",
    "    label_vocab.add(label)\n",
    "    for word in sent:\n",
    "        word_vocab.add(word)\n",
    "\n",
    "print(word_vocab, word_vocab.idx2item, word_vocab.item2idx)\n",
    "print(label_vocab, label_vocab.idx2item, label_vocab.item2idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class BoWModule(nn.Module):\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(BoWModule, self).__init__()\n",
    "        self.W = nn.Linear(input_size, output_size)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        return F.log_softmax(self.W(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Seq2Vec(object):\n",
    "    def __init__(self, vocab):\n",
    "        self.vocab = vocab\n",
    "        \n",
    "    def encode(self, seq):\n",
    "        vec = []\n",
    "        for item in seq:\n",
    "            vec.append(self.vocab.getidx(item))\n",
    "        return vec\n",
    "    \n",
    "    def batch_encode(self, seq_batch):\n",
    "        vecs = [self.encode(seq) for seq in seq_batch]\n",
    "        return vecs\n",
    "        \n",
    "        \n",
    "class Seq2OneHot(object):\n",
    "    def __init__(self, size):\n",
    "        self.size = size\n",
    "    \n",
    "    def encode(self, x, as_variable=False):\n",
    "        one_hot = torch.zeros(self.size)\n",
    "        for i in x:\n",
    "            one_hot[i] += 1\n",
    "        one_hot = one_hot.view(1, -1)\n",
    "        if as_variable:\n",
    "            return Variable(one_hot)\n",
    "        return one_hot\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3, 4, 5, 6], [7, 8, 9, 1], [10, 11, 12, 13, 14, 15, 16], [10, 8, 17, 18, 19, 20, 16, 9, 21, 22, 23, 13]] [[0], [1], [0], [1]] [[0, 11, 12, 0], [8, 17, 22, 0, 1]] [[0], [1]]\n",
      "\n",
      "\n",
      "Columns 0 to 12 \n",
      "    0     1     1     1     1     1     1     0     0     0     0     0     0\n",
      "\n",
      "Columns 13 to 23 \n",
      "    0     0     0     0     0     0     0     0     0     0     0\n",
      "[torch.FloatTensor of size 1x24]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sent_encoder = Seq2Vec(word_vocab)\n",
    "\n",
    "def data2vec(data):\n",
    "    X, y = [], []\n",
    "    for sent, label in data:\n",
    "        y.append([label_vocab.getidx(label)])\n",
    "        X.append(sent_encoder.encode(sent))\n",
    "    return X, y\n",
    "\n",
    "X_train, y_train = data2vec(data)\n",
    "X_test, y_test = data2vec(test_data) \n",
    "print(X_train, y_train, X_test, y_test)\n",
    "\n",
    "\n",
    "sent_onehot_encoder = Seq2OneHot(word_vocab.size)\n",
    "print(sent_onehot_encoder.encode(X_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log probs:  Variable containing:\n",
      "-0.9682 -0.4777\n",
      "[torch.FloatTensor of size 1x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = BoWModule(word_vocab.size, label_vocab.size)\n",
    "log_probs = model.forward(sent_onehot_encoder.encode(X_train[0], as_variable=True))\n",
    "print(\"Log probs: \", log_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_log_probs(log_probs, label_vocab, label_true=None):\n",
    "    for i, label_probs in enumerate(log_probs.data.tolist()):\n",
    "        prob_string = \", \".join([\n",
    "            \"{}: {:.3f}\".format(label_vocab.idx2item[j], val)\n",
    "            for j, val in enumerate(label_probs)\n",
    "        ])\n",
    "        true_string = \"?\"\n",
    "        if label_true is not None:\n",
    "            true_string = label_vocab.idx2item[label_true[i]]\n",
    "            \n",
    "        print(prob_string, \"True label: \", true_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPANISH: -0.968, ENGLISH: -0.478 True label:  ?\n"
     ]
    }
   ],
   "source": [
    "print_log_probs(log_probs, label_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPANISH: -1.428, ENGLISH: -0.274 True label:  SPANISH\n",
      "SPANISH: -0.807, ENGLISH: -0.591 True label:  ENGLISH\n"
     ]
    }
   ],
   "source": [
    "for seq, label in zip(X_test, y_test):\n",
    "    log_probs = model.forward(sent_onehot_encoder.encode(seq, as_variable=True))\n",
    "    print_log_probs(log_probs, label_vocab, label_true=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "-0.1235\n",
       "-0.0126\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(model.parameters())[:, word_vocab.getidx(\"creo\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_function = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "def get_loss(loss_function, seq, label, verbose=False):\n",
    "    ## Clear gradients before every update else memory runs out\n",
    "    model.zero_grad()\n",
    "    ## Do forward pass\n",
    "    log_probs = model.forward(sent_onehot_encoder.encode(seq, as_variable=True))\n",
    "    if verbose:\n",
    "        print(log_probs)\n",
    "    ## Get labels\n",
    "    target  = Variable(torch.LongTensor(label))\n",
    "    if verbose:\n",
    "        print(target)\n",
    "    ## Get loss\n",
    "    loss = loss_function(log_probs, target)\n",
    "    if verbose:\n",
    "        print(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "-0.9682 -0.4777\n",
      "[torch.FloatTensor of size 1x2]\n",
      "\n",
      "Variable containing:\n",
      " 0\n",
      "[torch.LongTensor of size 1]\n",
      "\n",
      "Variable containing:\n",
      " 0.9682\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss = get_loss(loss_function, X_train[0], y_train[0], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9681910276412964"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 263.87it/s]\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in tqdm(range(100)):\n",
    "    for seq, label in zip(X_train, y_train):\n",
    "        loss = get_loss(loss_function, seq, label)\n",
    "        losses.append(loss.data[0])\n",
    "        ## Get gradients of model params wrt. loss\n",
    "        loss.backward()\n",
    "        ## Optimize the loss by one step\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f12143902b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/napsternxg/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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bIy88jDaEDgAAAMQq1ysfiM4fCOo///Chao6c1L//4OJOhsupfAAAAMAcVD5i1OHaRtUc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\nEEIIIYQQQvQLCT6EEEIIIYQQ/eL/A/A6Z6cJ1YNSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f121a66bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPANISH: -0.307, ENGLISH: -1.331 True label:  SPANISH\n",
      "SPANISH: -3.080, ENGLISH: -0.047 True label:  ENGLISH\n"
     ]
    }
   ],
   "source": [
    "for seq, label in zip(X_test, y_test):\n",
    "    log_probs = model.forward(sent_onehot_encoder.encode(seq, as_variable=True))\n",
    "    print_log_probs(log_probs, label_vocab, label_true=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embedding(24, 5)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings = nn.Embedding(word_vocab.size, 5)\n",
    "word_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "  1.1375 -1.0246 -1.0300 -1.0129  0.0055\n",
       " -0.9347 -0.9882  1.3801 -0.1173  0.9317\n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "[torch.FloatTensor of size 1x4x5]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings(Variable(torch.LongTensor([X_test[0]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 11, 12, 0]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "-0.1278 -0.5443  0.5069 -0.9442  0.2694\n",
       "[torch.FloatTensor of size 1x5]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings(Variable(torch.LongTensor([X_test[0]]))).mean(1).view(-1, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings.embedding_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class BoEmbeddingsModule(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_size, output_size):\n",
    "        super(BoEmbeddingsModule, self).__init__()\n",
    "        self.word_embeddings = nn.Embedding(vocab_size, embedding_size)\n",
    "        self.W = nn.Linear(embedding_size, output_size)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        hidden_layer = self.word_embeddings(X).mean(1).view(-1,self.word_embeddings.embedding_dim)\n",
    "        return F.log_softmax(self.W(hidden_layer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log probs:  Variable containing:\n",
      "-0.8506 -0.5571\n",
      "[torch.FloatTensor of size 1x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = BoEmbeddingsModule(word_vocab.size, 5, label_vocab.size)\n",
    "log_probs = model.forward(Variable(torch.LongTensor([X_test[0]])))\n",
    "print(\"Log probs: \", log_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_function = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "def get_loss_embedding(loss_function, seq, label, verbose=False):\n",
    "    ## Clear gradients before every update else memory runs out\n",
    "    model.zero_grad()\n",
    "    ## Do forward pass\n",
    "    log_probs = model.forward(Variable(torch.LongTensor([seq])))\n",
    "    if verbose:\n",
    "        print(log_probs)\n",
    "    ## Get labels\n",
    "    target  = Variable(torch.LongTensor(label))\n",
    "    if verbose:\n",
    "        print(target)\n",
    "    ## Get loss\n",
    "    loss = loss_function(log_probs, target)\n",
    "    if verbose:\n",
    "        print(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "-0.9180 -0.5097\n",
      "[torch.FloatTensor of size 1x2]\n",
      "\n",
      "Variable containing:\n",
      " 0\n",
      "[torch.LongTensor of size 1]\n",
      "\n",
      "Variable containing:\n",
      " 0.9180\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss = get_loss_embedding(loss_function, X_train[0], y_train[0], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 335.84it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f121408a390>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/napsternxg/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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AAABgMFWs7Wr16tWaPXt2LnhI0owZMzRnzhytWrUq8N5EIiFJamhocFxvaGhQOp0eMq1E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\nt13ZxAJnPvwHzm3b0nu/L6JY1L4V3S+wuK/vb8s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67LBYkr0/+IO94Hx0Ue3KYVLtSi9Y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\nAAAAAHDEd2kwhpph7RJIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1219f90c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in tqdm(range(100)):\n",
    "    for seq, label in zip(X_train, y_train):\n",
    "        loss = get_loss_embedding(loss_function, seq, label)\n",
    "        losses.append(loss.data[0])\n",
    "        ## Get gradients of model params wrt. loss\n",
    "        loss.backward()\n",
    "        ## Optimize the loss by one step\n",
    "        optimizer.step()\n",
    "        \n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "  1.1375 -1.0246 -1.0300 -1.0129  0.0055\n",
       " -0.9347 -0.9882  1.3801 -0.1173  0.9317\n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "[torch.FloatTensor of size 1x4x5]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings(Variable(torch.LongTensor([X_test[0]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "\n",
       "(1 ,.,.) = \n",
       "  1.1375 -1.0246 -1.0300 -1.0129  0.0055\n",
       "\n",
       "(2 ,.,.) = \n",
       " -0.9347 -0.9882  1.3801 -0.1173  0.9317\n",
       "\n",
       "(3 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "[torch.FloatTensor of size 4x1x5]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_embeddings(Variable(torch.LongTensor([X_test[0]]))).permute(1, 0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lstm = nn.LSTM(5,2)\n",
    "input_units = word_embeddings(Variable(torch.LongTensor([X_test[0]]))).permute(1, 0, 2)\n",
    "hidden_units = (Variable(torch.randn(1,1,2)), Variable(torch.randn((1,1,2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTM(5, 2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "\n",
       "(1 ,.,.) = \n",
       "  1.1375 -1.0246 -1.0300 -1.0129  0.0055\n",
       "\n",
       "(2 ,.,.) = \n",
       " -0.9347 -0.9882  1.3801 -0.1173  0.9317\n",
       "\n",
       "(3 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "[torch.FloatTensor of size 4x1x5]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Variable containing:\n",
       " (0 ,.,.) = \n",
       "  -0.2699 -0.1599\n",
       " [torch.FloatTensor of size 1x1x2], Variable containing:\n",
       " (0 ,.,.) = \n",
       "  -0.0057  1.0992\n",
       " [torch.FloatTensor of size 1x1x2])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hidden_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       " -0.3569 -0.0822  0.8388 -1.3233  0.0701\n",
       "[torch.FloatTensor of size 1x1x5]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_units[:1, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       "  0.1496  2.4154 -0.8190  0.4201 -0.7981\n",
       "[torch.FloatTensor of size 1x1x5]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Variable(torch.randn((1,5))).view(1,1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Variable containing:\n",
       " (0 ,.,.) = \n",
       "  -0.0864  0.1397\n",
       " [torch.FloatTensor of size 1x1x2], (Variable containing:\n",
       "  (0 ,.,.) = \n",
       "   -0.0864  0.1397\n",
       "  [torch.FloatTensor of size 1x1x2], Variable containing:\n",
       "  (0 ,.,.) = \n",
       "   -0.2551  0.2165\n",
       "  [torch.FloatTensor of size 1x1x2]))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm(input_units[:1, :, :], hidden_units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Variable containing:\n",
       " (0 ,.,.) = \n",
       "  -0.0864  0.1397\n",
       " \n",
       " (1 ,.,.) = \n",
       "   0.1784 -0.0851\n",
       " \n",
       " (2 ,.,.) = \n",
       "  -0.1222 -0.2234\n",
       " \n",
       " (3 ,.,.) = \n",
       "  -0.1724 -0.3081\n",
       " [torch.FloatTensor of size 4x1x2], (Variable containing:\n",
       "  (0 ,.,.) = \n",
       "   -0.1724 -0.3081\n",
       "  [torch.FloatTensor of size 1x1x2], Variable containing:\n",
       "  (0 ,.,.) = \n",
       "   -0.5355 -0.4977\n",
       "  [torch.FloatTensor of size 1x1x2]))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm(input_units, hidden_units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Variable containing:\n",
       " (0 ,.,.) = \n",
       "  -0.0864  0.1397\n",
       " \n",
       " (1 ,.,.) = \n",
       "   0.1784 -0.0851\n",
       " \n",
       " (2 ,.,.) = \n",
       "  -0.1222 -0.2234\n",
       " \n",
       " (3 ,.,.) = \n",
       "  -0.1724 -0.3081\n",
       " [torch.FloatTensor of size 4x1x2], (Variable containing:\n",
       "  (0 ,.,.) = \n",
       "   -0.1724 -0.3081\n",
       "  [torch.FloatTensor of size 1x1x2], Variable containing:\n",
       "  (0 ,.,.) = \n",
       "   -0.5355 -0.4977\n",
       "  [torch.FloatTensor of size 1x1x2]))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out, hidden = lstm(input_units, hidden_units)\n",
    "out, hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "-0.1724 -0.3081\n",
       "[torch.FloatTensor of size 1x2]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out[-1, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "-0.6276 -0.7633\n",
       "[torch.FloatTensor of size 1x2]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.log_softmax(out[-1, :, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LSTMPredictor(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_size, hidden_size, output_size):\n",
    "        super(LSTMPredictor, self).__init__()\n",
    "        self.word_embeddings = nn.Embedding(vocab_size, embedding_size)\n",
    "        self.lstm = nn.LSTM(embedding_size, hidden_size)\n",
    "        self.output = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        seq_embed = self.word_embeddings(X).permute(1, 0, 2)\n",
    "        out, hidden = self.lstm(seq_embed)\n",
    "        output = self.output(out[-1, :, :])\n",
    "        return F.log_softmax(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log probs:  Variable containing:\n",
      "-0.7077 -0.6788\n",
      "[torch.FloatTensor of size 1x2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = LSTMPredictor(word_vocab.size, 5, 3, label_vocab.size)\n",
    "log_probs = model.forward(Variable(torch.LongTensor([X_test[0]])))\n",
    "print(\"Log probs: \", log_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_function = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "def get_loss_embedding_lstm(model, loss_function, seq, label, verbose=False):\n",
    "    ## Clear gradients before every update else memory runs out\n",
    "    model.zero_grad()\n",
    "    ## Do forward pass\n",
    "    log_probs = model.forward(Variable(torch.LongTensor([seq])))\n",
    "    if verbose:\n",
    "        print(log_probs)\n",
    "    ## Get labels\n",
    "    target  = Variable(torch.LongTensor(label))\n",
    "    if verbose:\n",
    "        print(target)\n",
    "    ## Get loss\n",
    "    loss = loss_function(log_probs, target)\n",
    "    if verbose:\n",
    "        print(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "-0.4848 -0.9566\n",
      "[torch.FloatTensor of size 1x2]\n",
      "\n",
      "Variable containing:\n",
      " 0\n",
      "[torch.LongTensor of size 1]\n",
      "\n",
      "Variable containing:\n",
      " 0.4848\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss = get_loss_embedding_lstm(model, loss_function, X_train[0], y_train[0], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 73.65it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f121424f860>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/napsternxg/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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UDJx7zXY4Kh8GW+0CAAAgvQgfPpbqE867nulR+aDtCgAAAGlG+PCxVBwy6BUy7KHEsG3D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\nqFv8P7KjgJPBlZRGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12143377b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in tqdm(range(100)):\n",
    "    for seq, label in zip(X_train, y_train):\n",
    "        loss = get_loss_embedding_lstm(model, loss_function, seq, label)\n",
    "        losses.append(loss.data[0])\n",
    "        ## Get gradients of model params wrt. loss\n",
    "        loss.backward()\n",
    "        ## Optimize the loss by one step\n",
    "        optimizer.step()\n",
    "        \n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## POS Tagger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_data = [\n",
    "    (\"The dog ate the apple\".split(), [\"DET\", \"NN\", \"V\", \"DET\", \"NN\"]),\n",
    "    (\"Everybody read that book\".split(), [\"NN\", \"V\", \"DET\", \"NN\"])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocab(name=words, size=10, UNK=UNK, offset=1) ['UNK', 'The', 'dog', 'ate', 'the', 'apple', 'Everybody', 'read', 'that', 'book'] {'Everybody': 6, 'book': 9, 'dog': 2, 'read': 7, 'the': 4, 'UNK': 0, 'that': 8, 'The': 1, 'apple': 5, 'ate': 3}\n",
      "Vocab(name=pos_tags, size=3, UNK=None, offset=0) ['DET', 'NN', 'V'] {'DET': 0, 'NN': 1, 'V': 2}\n"
     ]
    }
   ],
   "source": [
    "word_vocab = Vocab(\"words\", UNK=\"UNK\")\n",
    "pos_vocab = Vocab(\"pos_tags\")\n",
    "\n",
    "for sent, pos_tags in training_data:\n",
    "    for word, pos in zip(sent, pos_tags):\n",
    "        word_vocab.add(word)\n",
    "        pos_vocab.add(pos)\n",
    "\n",
    "print(word_vocab, word_vocab.idx2item, word_vocab.item2idx)\n",
    "print(pos_vocab, pos_vocab.idx2item, pos_vocab.item2idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3, 4, 5], [6, 7, 8, 9]] [[0, 1, 2, 0, 1], [1, 2, 0, 1]]\n"
     ]
    }
   ],
   "source": [
    "sent_encoder = Seq2Vec(word_vocab)\n",
    "pos_encoder = Seq2Vec(pos_vocab)\n",
    "\n",
    "def dataseq2vec(data):\n",
    "    X, Y = [], []\n",
    "    for sent, tags in data:\n",
    "        X.append(sent_encoder.encode(sent))\n",
    "        Y.append(pos_encoder.encode(tags))\n",
    "    return X, Y\n",
    "\n",
    "X_train, Y_train = dataseq2vec(training_data)\n",
    "print(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LSTMTagger(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_size, hidden_size, output_size):\n",
    "        super(LSTMTagger, self).__init__()\n",
    "        self.word_embeddings = nn.Embedding(vocab_size, embedding_size)\n",
    "        self.lstm = nn.LSTM(embedding_size, hidden_size)\n",
    "        self.output = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        seq_embed = self.word_embeddings(X).permute(1, 0, 2)\n",
    "        out, hidden = self.lstm(seq_embed)\n",
    "        # Reshape the output to be a tensor of shape seq_len*label_size\n",
    "        output = self.output(out.view(X.data.size(1), -1))\n",
    "        return F.log_softmax(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vocab(name=words, size=10, UNK=UNK, offset=1)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log probs:  Variable containing:\n",
      "-0.8773 -0.9818 -1.5632\n",
      "-0.8260 -1.0724 -1.5140\n",
      "-0.8364 -1.0424 -1.5412\n",
      "-0.8753 -1.0138 -1.5123\n",
      "-0.8187 -1.1284 -1.4462\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = LSTMTagger(word_vocab.size, 5, 3, pos_vocab.size)\n",
    "log_probs = model.forward(Variable(torch.LongTensor([X_train[0]])))\n",
    "print(\"Log probs: \", log_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_function = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "def get_loss_embedding_lstm_tagger(model, loss_function, seq, label, verbose=False):\n",
    "    ## Clear gradients before every update else memory runs out\n",
    "    model.zero_grad()\n",
    "    ## Do forward pass\n",
    "    log_probs = model.forward(Variable(torch.LongTensor([seq])))\n",
    "    if verbose:\n",
    "        print(log_probs)\n",
    "    ## Get labels\n",
    "    target  = Variable(torch.LongTensor(label))\n",
    "    if verbose:\n",
    "        print(target)\n",
    "    ## Get loss\n",
    "    loss = loss_function(log_probs, target)\n",
    "    if verbose:\n",
    "        print(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "-0.8773 -0.9818 -1.5632\n",
      "-0.8260 -1.0724 -1.5140\n",
      "-0.8364 -1.0424 -1.5412\n",
      "-0.8753 -1.0138 -1.5123\n",
      "-0.8187 -1.1284 -1.4462\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n",
      "Variable containing:\n",
      " 0\n",
      " 1\n",
      " 2\n",
      " 0\n",
      " 1\n",
      "[torch.LongTensor of size 5]\n",
      "\n",
      "Variable containing:\n",
      " 1.0989\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss = get_loss_embedding_lstm_tagger(model, loss_function, X_train[0], Y_train[0], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:00<00:00, 178.26it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f12141bf668>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/napsternxg/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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gcSE63bCQ+QDyciuzI1VaamXO9VDdjeJaf7fDDJ+3BPGhvo7jWHdIOvayrbYhWFRhos4t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532MBgMhgMFx65fgWM3rMDPt/0aL3reOu8eM6mrBIH4L9w1aZq0KgjF8+OIhLs3i/TVxq5Cb4c2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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12142c5940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in tqdm(range(100)):\n",
    "    for seq, label in zip(X_train, Y_train):\n",
    "        loss = get_loss_embedding_lstm_tagger(model, loss_function, seq, label)\n",
    "        losses.append(loss.data[0])\n",
    "        ## Get gradients of model params wrt. loss\n",
    "        loss.backward()\n",
    "        ## Optimize the loss by one step\n",
    "        optimizer.step()\n",
    "        \n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Char embeddings + Word embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocab(name=words, size=10, UNK=UNK, offset=1) ['UNK', 'The', 'dog', 'ate', 'the', 'apple', 'Everybody', 'read', 'that', 'book'] {'Everybody': 6, 'book': 9, 'dog': 2, 'read': 7, 'the': 4, 'UNK': 0, 'that': 8, 'The': 1, 'apple': 5, 'ate': 3}\n",
      "Vocab(name=chars, size=17, UNK=<U>, offset=1) ['<U>', 'T', 'h', 'e', 'd', 'o', 'g', 'a', 't', 'p', 'l', 'E', 'v', 'r', 'y', 'b', 'k'] {'o': 5, 'd': 4, '<U>': 0, 'r': 13, 'v': 12, 'b': 15, 't': 8, 'a': 7, 'y': 14, 'l': 10, 'k': 16, 'e': 3, 'g': 6, 'p': 9, 'T': 1, 'h': 2, 'E': 11}\n",
      "Vocab(name=pos_tags, size=3, UNK=None, offset=0) ['DET', 'NN', 'V'] {'DET': 0, 'NN': 1, 'V': 2}\n"
     ]
    }
   ],
   "source": [
    "word_vocab = Vocab(\"words\", UNK=\"UNK\")\n",
    "char_vocab = Vocab(\"chars\", UNK=\"<U>\")\n",
    "pos_vocab = Vocab(\"pos_tags\")\n",
    "\n",
    "for sent, pos_tags in training_data:\n",
    "    for word, pos in zip(sent, pos_tags):\n",
    "        word_vocab.add(word)\n",
    "        pos_vocab.add(pos)\n",
    "        char_vocab.batch_add(word)\n",
    "\n",
    "print(word_vocab, word_vocab.idx2item, word_vocab.item2idx)\n",
    "print(char_vocab, char_vocab.idx2item, char_vocab.item2idx)\n",
    "print(pos_vocab, pos_vocab.idx2item, pos_vocab.item2idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3, 4, 5], [6, 7, 8, 9]] [[[1, 2, 3], [4, 5, 6], [7, 8, 3], [8, 2, 3], [7, 9, 9, 10, 3]], [[11, 12, 3, 13, 14, 15, 5, 4, 14], [13, 3, 7, 4], [8, 2, 7, 8], [15, 5, 5, 16]]] [[0, 1, 2, 0, 1], [1, 2, 0, 1]]\n"
     ]
    }
   ],
   "source": [
    "sent_encoder = Seq2Vec(word_vocab)\n",
    "char_encoder = Seq2Vec(char_vocab)\n",
    "pos_encoder = Seq2Vec(pos_vocab)\n",
    "\n",
    "def dataseqchar2vec(data):\n",
    "    X, X_char, Y = [], [], []\n",
    "    for sent, tags in data:\n",
    "        X.append(sent_encoder.encode(sent))\n",
    "        X_char.append(char_encoder.batch_encode(sent))\n",
    "        Y.append(pos_encoder.encode(tags))\n",
    "    return X, X_char, Y\n",
    "\n",
    "X_train, X_char_train, Y_train = dataseqchar2vec(training_data)\n",
    "print(X_train, X_char_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CharEmbedding(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_size,\n",
    "                 out_channels, kernel_sizes, dropout=0.5):\n",
    "        super(CharEmbedding, self).__init__()\n",
    "        self.char_embeddings = nn.Embedding(vocab_size, embedding_size)\n",
    "        self.convs1 = [nn.Conv2d(1, out_channels, (K, embedding_size)) \n",
    "                       for K in kernel_sizes]\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, X):\n",
    "        x = self.char_embeddings(X)\n",
    "        # Ref: https://github.com/Shawn1993/cnn-text-classification-pytorch/blob/master/model.py\n",
    "        x = x.unsqueeze(1) # (N,Ci,W,D)\n",
    "        x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] #[(N,Co,W), ...]*len(Ks)\n",
    "        x = [F.max_pool1d(self.dropout(i), i.size(2)).squeeze(2) for i in x] #[(N,Co), ...]*len(Ks)\n",
    "        x = torch.cat(x, 1)\n",
    "        \n",
    "        return self.dropout(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 8, 3]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_char_train[0][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 2.7443  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.6250\n",
       "[torch.FloatTensor of size 1x8]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_embedding = CharEmbedding(char_vocab.size, 5, 4, [2, 3])\n",
    "char_embedding(Variable(torch.LongTensor([X_char_train[0][2]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       "  0.0000  0.0000  0.0000  0.0000  2.4643  0.0000  0.0000  0.0000\n",
       "  0.0000  0.0000  0.0000  2.9619  0.0000  0.0000  0.0000  0.0000\n",
       "  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  5.5457  0.0000\n",
       "  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.5848\n",
       "  2.9610  0.0000  1.0892  4.7171  2.7581  0.0000  5.3865  4.0242\n",
       "[torch.FloatTensor of size 1x5x8]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cat([\n",
    "    char_embedding(Variable(torch.LongTensor([x]))).unsqueeze(0)\n",
    "    for x in X_char_train[0]\n",
    "          ], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class WordCharEmbedding(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_size,\n",
    "                 char_embedding_model, dropout=0.5):\n",
    "        super(WordCharEmbedding, self).__init__()\n",
    "        self.char_embeddings = char_embedding_model\n",
    "        self.word_embeddings = nn.Embedding(vocab_size, embedding_size)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, X, X_char=None):\n",
    "        # Ref: https://github.com/Shawn1993/cnn-text-classification-pytorch/blob/master/model.py\n",
    "        word_vecs = self.word_embeddings(X)\n",
    "        if X_char is not None:\n",
    "            char_vecs = torch.cat([\n",
    "                self.char_embeddings(x).unsqueeze(0)\n",
    "                for x in X_char\n",
    "            ], 1)\n",
    "            word_vecs = char_vecs + word_vecs\n",
    "        return self.dropout(word_vecs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1\n",
       " 2\n",
       " 3\n",
       " 4\n",
       " 5\n",
       "[torch.FloatTensor of size 5]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Variable(torch.Tensor(X_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def charseq2varlist(X_chars):\n",
    "    return [Variable(torch.LongTensor([x])) for x in X_chars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       "(0 ,.,.) = \n",
       "  5.8556  1.2501  1.5115  0.3571  1.3092  1.2664 -0.7241 -0.2907\n",
       " -0.1620 -0.3580 -0.1261 -0.3806  1.0674  0.5853 -0.9918 -1.2741\n",
       "  0.5484  0.4085 -0.2132 -0.0653 -0.2304 -0.3716  0.0516  0.6814\n",
       "  0.6918  1.7022 -0.2582  0.5488  0.8628  0.6860  2.2349 -1.1200\n",
       "  1.6643 -0.8964  6.1735  3.5807  0.7116  1.0542  3.0053  0.1171\n",
       "[torch.FloatTensor of size 1x5x8]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_char_embedding = WordCharEmbedding(word_vocab.size, 8, char_embedding, dropout=0)\n",
    "word_char_embedding(Variable(torch.LongTensor([X_train[0]])), charseq2varlist(X_char_train[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LSTMTaggerWordChar(nn.Module):\n",
    "    def __init__(self, word_char_embedding, embedding_size, hidden_size, output_size):\n",
    "        super(LSTMTaggerWordChar, self).__init__()\n",
    "        self.word_embeddings = word_char_embedding\n",
    "        self.lstm = nn.LSTM(embedding_size, hidden_size//2, bidirectional=True)\n",
    "        self.output = nn.Linear(hidden_size, output_size)\n",
    "        \n",
    "    def forward(self, X, X_char):\n",
    "        seq_embed = self.word_embeddings(X, X_char).permute(1, 0, 2)\n",
    "        out, hidden = self.lstm(seq_embed)\n",
    "        # Reshape the output to be a tensor of shape seq_len*label_size\n",
    "        output = self.output(out.view(X.data.size(1), -1))\n",
    "        return F.log_softmax(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log probs:  Variable containing:\n",
      "-0.9419 -1.4267 -0.9942\n",
      "-0.9549 -1.4888 -0.9428\n",
      "-0.9090 -1.4640 -1.0058\n",
      "-0.9294 -1.4696 -0.9803\n",
      "-0.8702 -1.5029 -1.0254\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = LSTMTaggerWordChar(word_char_embedding, 8, 4, pos_vocab.size)\n",
    "log_probs = model.forward(Variable(torch.LongTensor([X_train[0]])), charseq2varlist(X_char_train[0]))\n",
    "print(\"Log probs: \", log_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loss_function = nn.NLLLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "def get_loss_embedding_lstm_tagger_word_char(model, loss_function, seq, seq_char, label, verbose=False):\n",
    "    ## Clear gradients before every update else memory runs out\n",
    "    model.zero_grad()\n",
    "    ## Do forward pass\n",
    "    log_probs = model.forward(Variable(torch.LongTensor([seq])),\n",
    "                              charseq2varlist(seq_char))\n",
    "    if verbose:\n",
    "        print(log_probs)\n",
    "    ## Get labels\n",
    "    target  = Variable(torch.LongTensor(label))\n",
    "    if verbose:\n",
    "        print(target)\n",
    "    ## Get loss\n",
    "    loss = loss_function(log_probs, target)\n",
    "    if verbose:\n",
    "        print(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      "-0.9460 -1.4277 -0.9893\n",
      "-0.9797 -1.4607 -0.9353\n",
      "-0.9360 -1.4737 -0.9709\n",
      "-0.9361 -1.4140 -1.0088\n",
      "-1.0441 -1.2930 -0.9847\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n",
      "Variable containing:\n",
      " 0\n",
      " 1\n",
      " 2\n",
      " 0\n",
      " 1\n",
      "[torch.LongTensor of size 5]\n",
      "\n",
      "Variable containing:\n",
      " 1.1213\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss = get_loss_embedding_lstm_tagger_word_char(model, loss_function, X_train[0], X_char_train[0], Y_train[0], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 56.15it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f12142a45c0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/napsternxg/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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xF2aEn9HFoObxpSh3Ujq75HZQAFW3q/YXXOWKxiLGA7pA6rS4jgr5tRzssisS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\nXNe+8yHev+58qLpd0VyM+u+shSh3pJ3S/MquAjkfirp7v1kfdIFXdz6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E8+EXJ8MDJenK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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f121433ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in tqdm(range(100)):\n",
    "    for seq, seq_char, label in zip(X_train, X_char_train, Y_train):\n",
    "        loss = get_loss_embedding_lstm_tagger_word_char(model, loss_function, seq, seq_char, label)\n",
    "        losses.append(loss.data[0])\n",
    "        ## Get gradients of model params wrt. loss\n",
    "        loss.backward()\n",
    "        ## Optimize the loss by one step\n",
    "        optimizer.step()\n",
    "        \n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CRF model\n",
    "\n",
    "Adopted from http://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 4.4402\n",
       "[torch.FloatTensor of size 1x1]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_scalar(var):\n",
    "    # returns a python float\n",
    "    return var.view(-1).data.tolist()[0]\n",
    "\n",
    "\n",
    "def argmax(vec):\n",
    "    # return the argmax as a python int\n",
    "    _, idx = torch.max(vec, 1)\n",
    "    return to_scalar(idx)\n",
    "\n",
    "\n",
    "def log_sum_exp_torch(vecs, axis=None):\n",
    "    ## Use help from: http://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py\n",
    "    if axis < 0:\n",
    "        axis = vecs.ndimension()+axis\n",
    "    max_val, _ = vecs.max(axis)\n",
    "    vecs = vecs - max_val.expand_as(vecs)\n",
    "    out_val = torch.log(torch.exp(vecs).sum(axis))\n",
    "    #print(max_val, out_val)\n",
    "    return max_val + out_val\n",
    "\n",
    "log_sum_exp_torch(Variable(torch.Tensor([[1,2,3,4]])), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CRFLayer(nn.Module):\n",
    "    def __init__(self, num_labels):\n",
    "        super(CRFLayer, self).__init__()\n",
    "        self.num_labels = num_labels\n",
    "        self.transitions = nn.Parameter(torch.randn(self.num_labels, self.num_labels))\n",
    "        \n",
    "    def _forward_alg(self, emissions):\n",
    "        scores = emissions[0]\n",
    "        # Get the log sum exp score\n",
    "        transitions = self.transitions.transpose(-1,-2)\n",
    "        for i in range(1, emissions.size(0)):\n",
    "            scores = emissions[i] + log_sum_exp_torch(\n",
    "                scores.expand_as(transitions) + transitions,\n",
    "                axis=1)\n",
    "        return log_sum_exp_torch(scores, axis=-1)\n",
    "        \n",
    "    def _score_sentence(self, emissions, tags):\n",
    "        score = emissions[0][tags[0]]\n",
    "        for i, emission in enumerate(emissions[1:]):\n",
    "            score = score + self.transitions[tags[i], tags[i+1]] + emission[tags[i+1]]\n",
    "        return score\n",
    "    \n",
    "    def _viterbi_decode(self, emissions):\n",
    "        scores = torch.zeros(emissions.size(1))\n",
    "        back_pointers = torch.zeros(emissions.size()).int()\n",
    "        scores = scores + emissions[0]\n",
    "        transitions = self.transitions\n",
    "        # Generate most likely scores and paths for each step in sequence\n",
    "        for i in range(1, emissions.size(0)):\n",
    "            scores_with_transitions = scores.unsqueeze(1).expand_as(transitions) + transitions\n",
    "            max_scores, back_pointers[i] = torch.max(scores_with_transitions, 0)\n",
    "            scores = emissions[i] + max_scores\n",
    "        # Generate the most likely path\n",
    "        viterbi = [scores.numpy().argmax()]\n",
    "        back_pointers = back_pointers.numpy()\n",
    "        for bp in reversed(back_pointers[1:]):\n",
    "            viterbi.append(bp[viterbi[-1]])\n",
    "        viterbi.reverse()\n",
    "        viterbi_score = scores.numpy().max()\n",
    "        return viterbi_score, viterbi\n",
    "        \n",
    "    def neg_log_likelihood(self, feats, tags):\n",
    "        forward_score = self._forward_alg(feats)\n",
    "        gold_score = self._score_sentence(feats, tags)\n",
    "        return forward_score - gold_score\n",
    "        \n",
    "    def forward(self, feats):\n",
    "        # Find the best path, given the features.\n",
    "        score, tag_seq = self._viterbi_decode(feats)\n",
    "        return score, tag_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LSTMTaggerWordCharCRF(nn.Module):\n",
    "    def __init__(self, word_char_embedding, embedding_size, hidden_size, output_size):\n",
    "        super(LSTMTaggerWordCharCRF, self).__init__()\n",
    "        self.word_embeddings = word_char_embedding\n",
    "        self.lstm = nn.LSTM(embedding_size, hidden_size//2, bidirectional=True)\n",
    "        self.output = nn.Linear(hidden_size, output_size)\n",
    "        self.crf = CRFLayer(output_size)\n",
    "        \n",
    "    def forward(self, X, X_char):\n",
    "        seq_embed = self.word_embeddings(X, X_char).permute(1, 0, 2)\n",
    "        out, hidden = self.lstm(seq_embed)\n",
    "        # Reshape the output to be a tensor of shape seq_len*label_size\n",
    "        output = self.output(out.view(X.data.size(1), -1))\n",
    "        return output\n",
    "    \n",
    "    def loss(self, X, X_char, Y):\n",
    "        feats = self.forward(X, X_char)\n",
    "        return self.crf.neg_log_likelihood(feats, Y)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:  Variable containing:\n",
      " 0.4043 -0.3115 -0.2113\n",
      " 0.4298 -0.4051 -0.2938\n",
      " 0.4460 -0.3561 -0.3785\n",
      " 0.3093 -0.2908 -0.4155\n",
      " 0.4780 -0.2837 -0.1906\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = LSTMTaggerWordCharCRF(word_char_embedding, 8, 4, pos_vocab.size)\n",
    "features = model.forward(Variable(torch.LongTensor([X_train[0]])), charseq2varlist(X_char_train[0]))\n",
    "print(\"Features: \", features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = optim.SGD([\n",
    "    {\"params\": model.parameters()},\n",
    "], lr=0.1)\n",
    "\n",
    "def get_loss_embedding_lstm_tagger_word_char_crf(model, seq, seq_char, label, verbose=False):\n",
    "    ## Clear gradients before every update else memory runs out\n",
    "    model.zero_grad()\n",
    "    ## Do forward pass\n",
    "    X, X_char = Variable(torch.LongTensor([seq])), charseq2varlist(seq_char)\n",
    "    ## Get labels\n",
    "    target  = torch.LongTensor(label)\n",
    "    if verbose:\n",
    "        print(target)\n",
    "    ## Get loss\n",
    "    loss = model.loss(X, X_char, target)\n",
    "    if verbose:\n",
    "        print(loss)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 0\n",
      " 1\n",
      " 2\n",
      " 0\n",
      " 1\n",
      "[torch.LongTensor of size 5]\n",
      "\n",
      "Variable containing:\n",
      " 8.7042\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss = get_loss_embedding_lstm_tagger_word_char_crf(model, X_train[0], X_char_train[0], Y_train[0], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:01<00:00, 54.59it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f121403d780>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/napsternxg/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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ID37MfcelM9XQeQAAAICPER78WHTEOcIDMw8AAADwMcKDH6PzAAAAAH9CePBjrQPTrSyW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\nf3i+49T54b7qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f120c212b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = []\n",
    "for epoch in tqdm(range(100)):\n",
    "    for seq, seq_char, label in zip(X_train, X_char_train, Y_train):\n",
    "        loss = get_loss_embedding_lstm_tagger_word_char_crf(model, seq, seq_char, label)\n",
    "        losses.append(loss.data[0])\n",
    "        ## Get gradients of model params wrt. loss\n",
    "        loss.backward()\n",
    "        ## Optimize the loss by one step\n",
    "        optimizer.step()\n",
    "        \n",
    "plt.plot(losses)\n",
    "plt.xlabel(\"Step\")\n",
    "plt.ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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