sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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    "colab": {},
    "colab_type": "code",
    "id": "h0YUKx5qZk3_"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "with open('reviews.txt','r') as f:\n",
    "  reviews = f.read()\n",
    "with open('labels.txt','r') as f:\n",
    "  labels = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-V29jzDsZ2wX"
   },
   "outputs": [],
   "source": [
    "from string import punctuation\n",
    "reviews = reviews.lower()\n",
    "all_text = ''.join([c for c in reviews if c not in punctuation])\n",
    "# split by new lines and spaces\n",
    "reviews_split = all_text.split('\\n')\n",
    "all_text = ' '.join(reviews_split)\n",
    "\n",
    "# create a list of words\n",
    "words = all_text.split()\n",
    "from collections import Counter\n",
    "\n",
    "## Build a dictionary that maps words to integers\n",
    "counts = Counter(words)\n",
    "vocab = sorted(counts, key=counts.get, reverse=True)\n",
    "vocab_to_int = {word: ii for ii, word in enumerate(vocab, 1)}\n",
    "\n",
    "## use the dict to tokenize each review in reviews_split\n",
    "## store the tokenized reviews in reviews_ints\n",
    "reviews_ints = []\n",
    "for review in reviews_split:\n",
    "    reviews_ints.append([vocab_to_int[word] for word in review.split()])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sdoLvaQgaMf3"
   },
   "outputs": [],
   "source": [
    "# 1=positive, 0=negative label conversion\n",
    "labels_split = labels.split('\\n')\n",
    "encoded_labels = np.array([1 if label == 'positive' else 0 for label in labels_split])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "tebfwXeVaOuH",
    "outputId": "9db3e700-58f0-4bbd-9180-b6456eea014d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-length reviews: 1\n",
      "Maximum review length: 2514\n",
      "Number of reviews before removing outliers:  25001\n",
      "Number of reviews after removing outliers:  25000\n"
     ]
    }
   ],
   "source": [
    "# outlier review stats\n",
    "review_lens = Counter([len(x) for x in reviews_ints])\n",
    "print(\"Zero-length reviews: {}\".format(review_lens[0]))\n",
    "print(\"Maximum review length: {}\".format(max(review_lens)))\n",
    "\n",
    "print('Number of reviews before removing outliers: ', len(reviews_ints))\n",
    "\n",
    "## remove any reviews/labels with zero length from the reviews_ints list.\n",
    "\n",
    "# get indices of any reviews with length 0\n",
    "non_zero_idx = [ii for ii, review in enumerate(reviews_ints) if len(review) != 0]\n",
    "\n",
    "# remove 0-length reviews and their labels\n",
    "reviews_ints = [reviews_ints[ii] for ii in non_zero_idx]\n",
    "encoded_labels = np.array([encoded_labels[ii] for ii in non_zero_idx])\n",
    "\n",
    "print('Number of reviews after removing outliers: ', len(reviews_ints))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "colab_type": "code",
    "id": "8ZixNIiTaUz_",
    "outputId": "75a5749a-9417-4920-9f43-054bb2229c54"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [22382    42 46418    15   706 17139  3389    47    77    35]\n",
      " [ 4505   505    15     3  3342   162  8312  1652     6  4819]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [   54    10    14   116    60   798   552    71   364     5]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    1   330   578    34     3   162   748  2731     9   325]\n",
      " [    9    11 10171  5305  1946   689   444    22   280   673]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    1   307 10399  2069  1565  6202  6528  3288 17946 10628]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [   21   122  2069  1565   515  8181    88     6  1325  1182]\n",
      " [    1    20     6    76    40     6    58    81    95     5]\n",
      " [   54    10    84   329 26230 46427    63    10    14   614]\n",
      " [   11    20     6    30  1436 32317  3769   690 15100     6]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [   40    26   109 17952  1422     9     1   327     4   125]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [   10   499     1   307 10399    55    74     8    13    30]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]\n",
      " [    0     0     0     0     0     0     0     0     0     0]]\n"
     ]
    }
   ],
   "source": [
    "def pad_features(reviews_ints, seq_length):\n",
    "    ''' Return features of review_ints, where each review is padded with 0's \n",
    "        or truncated to the input seq_length.\n",
    "    '''\n",
    "    \n",
    "    # getting the correct rows x cols shape\n",
    "    features = np.zeros((len(reviews_ints), seq_length), dtype=int)\n",
    "\n",
    "    # for each review, I grab that review and \n",
    "    for i, row in enumerate(reviews_ints):\n",
    "        features[i, -len(row):] = np.array(row)[:seq_length]\n",
    "    \n",
    "    return features\n",
    "seq_length = 200\n",
    "\n",
    "features = pad_features(reviews_ints, seq_length=seq_length)\n",
    "\n",
    "assert len(features)==len(reviews_ints), \"Your features should have as many rows as reviews.\"\n",
    "assert len(features[0])==seq_length, \"Each feature row should contain seq_length values.\"\n",
    "\n",
    "# print first 10 values of the first 30 batches \n",
    "print(features[:30,:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "THIGZiQvaiAj",
    "outputId": "84c2741e-3577-4de9-df73-9cd10e7017b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tFeature Shapes:\n",
      "Train set: \t\t(20000, 200) \n",
      "Validation set: \t(2500, 200) \n",
      "Test set: \t\t(2500, 200)\n"
     ]
    }
   ],
   "source": [
    "split_frac = 0.8\n",
    "\n",
    "## split data into training, validation, and test data (features and labels, x and y)\n",
    "\n",
    "split_idx = int(len(features)*0.8)\n",
    "train_x, remaining_x = features[:split_idx], features[split_idx:]\n",
    "train_y, remaining_y = encoded_labels[:split_idx], encoded_labels[split_idx:]\n",
    "\n",
    "test_idx = int(len(remaining_x)*0.5)\n",
    "val_x, test_x = remaining_x[:test_idx], remaining_x[test_idx:]\n",
    "val_y, test_y = remaining_y[:test_idx], remaining_y[test_idx:]\n",
    "\n",
    "## print out the shapes of your resultant feature data\n",
    "print(\"\\t\\t\\tFeature Shapes:\")\n",
    "print(\"Train set: \\t\\t{}\".format(train_x.shape), \n",
    "      \"\\nValidation set: \\t{}\".format(val_x.shape),\n",
    "      \"\\nTest set: \\t\\t{}\".format(test_x.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VWqi72rZakCF"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "# create Tensor datasets\n",
    "train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y))\n",
    "valid_data = TensorDataset(torch.from_numpy(val_x), torch.from_numpy(val_y))\n",
    "test_data = TensorDataset(torch.from_numpy(test_x), torch.from_numpy(test_y))\n",
    "\n",
    "# dataloaders\n",
    "batch_size = 50\n",
    "\n",
    "# make sure the SHUFFLE your training data\n",
    "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)\n",
    "valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size)\n",
    "test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "59-_fqhAaoxT",
    "outputId": "fee99b71-a88c-4e95-9821-cfbbb4b5eae1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample input size:  torch.Size([50, 200])\n",
      "Sample input: \n",
      " tensor([[    0,     0,     0,  ...,    58,   841,   135],\n",
      "        [   11,     6,   179,  ...,  1350, 14809,   167],\n",
      "        [ 1011,  1377,     7,  ...,  2962,    12,    10],\n",
      "        ...,\n",
      "        [   54,    10,    84,  ...,    32,   117,     3],\n",
      "        [   11,    18,     6,  ...,  6009,     4,     1],\n",
      "        [    0,     0,     0,  ...,     3,   759,  2383]])\n",
      "\n",
      "Sample label size:  torch.Size([50])\n",
      "Sample label: \n",
      " tensor([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0,\n",
      "        0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
      "        1, 1])\n"
     ]
    }
   ],
   "source": [
    "# obtain one batch of training data\n",
    "dataiter = iter(train_loader)\n",
    "sample_x, sample_y = dataiter.next()\n",
    "\n",
    "print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n",
    "print('Sample input: \\n', sample_x)\n",
    "print()\n",
    "print('Sample label size: ', sample_y.size()) # batch_size\n",
    "print('Sample label: \\n', sample_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "sjB40gV-ap2z",
    "outputId": "ee568880-1bd1-4d00-eb71-b1ff62f50fc9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU.\n"
     ]
    }
   ],
   "source": [
    "# First checking if GPU is available\n",
    "train_on_gpu=torch.cuda.is_available()\n",
    "\n",
    "if(train_on_gpu):\n",
    "    print('Training on GPU.')\n",
    "else:\n",
    "    print('No GPU available, training on CPU.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QRNN\n",
    "The implementation is based on the architecture presented in https://arxiv.org/abs/1611.01576. The QRNN combines the methodologies used in convolutional neural networks and recurrent neural networks to increase the performance of the architecture and increasing the computation speed. For this implementation, 2 layers are used with a kernel size of 2 and 256 filters in each layer. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3xKsO0Qyas7N"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class QRNNLayer(nn.Module):\n",
    "    def __init__(self,batch_size,input_size,n_filters,kernel_size,embed_size,device,dropout):\n",
    "        super(QRNNLayer,self).__init__()\n",
    "        self.batch_size = batch_size\n",
    "        self.input_size = input_size\n",
    "        self.n_filters = n_filters\n",
    "        self.kernel_size = kernel_size\n",
    "        self.embed_size = embed_size\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "        self.device = device\n",
    "        self.conv1 = torch.nn.Conv1d(self.input_size,self.n_filters,self.kernel_size)\n",
    "        self.conv2 = torch.nn.Conv1d(self.input_size,self.n_filters,self.kernel_size)\n",
    "        self.conv3 = torch.nn.Conv1d(self.input_size,self.n_filters,self.kernel_size)\n",
    "    \n",
    "    def forward(self,masked_input, h, c):\n",
    "        Z,F,O = self.masked_conv(masked_input)\n",
    "        h, c = self.pool(c,Z,F,O)\n",
    "        masked_input = h\n",
    "        return masked_input,h,c\n",
    "    def masked_conv(self,x):\n",
    "        pad = torch.zeros([self.batch_size,1,self.input_size],device=self.device)\n",
    "        x = torch.cat([pad,x],1).permute(0,2,1)\n",
    "        Z = torch.tanh((self.conv1(x)))\n",
    "        F = torch.sigmoid((self.conv2(x)))\n",
    "        O = torch.sigmoid((self.conv3(x)))\n",
    "        one_mask = torch.ones_like(F,device=self.device) - F\n",
    "        F = 1 - self.dropout(one_mask)\n",
    "        return Z.permute(0,2,1), F.permute(0,2,1), O.permute(0,2,1)\n",
    "    def pool(self, prev_c,Z,F,O):\n",
    "        c = torch.mul(F,prev_c) + torch.mul(1-F,Z)\n",
    "        h = torch.mul(O,c)\n",
    "        return h,c\n",
    "\n",
    "class QRNN(nn.Module):\n",
    "    def __init__(self,vocab_size,embed_size,n_filters,kernel_size,batch_size,seq_len,layers,device,dropout):\n",
    "        super(QRNN,self).__init__()\n",
    "        self.embed_size = embed_size\n",
    "        self.n_filters = n_filters\n",
    "        self.kernel_size = kernel_size\n",
    "        self.batch_size = batch_size\n",
    "        self.seq_len = seq_len\n",
    "        self.num_layer = layers\n",
    "        self.device = device\n",
    "        self.embedding = torch.nn.Embedding(vocab_size,embed_size)\n",
    "        self.dense = torch.nn.Linear(self.seq_len*self.n_filters,1)\n",
    "        self.QRNN_layers = torch.nn.ModuleList([QRNNLayer(self.batch_size,embed_size if l ==0 else n_filters,\n",
    "                                                         self.n_filters,self.kernel_size,self.embed_size,self.device,\n",
    "                                                         dropout,) for l in range(self.num_layer)])\n",
    "        \n",
    "        \n",
    "    def forward(self,x,target):\n",
    "        x = self.embedding(x)\n",
    "        h = torch.zeros([self.batch_size,self.seq_len,self.n_filters],device=self.device)\n",
    "        c = torch.zeros_like(h,device=self.device)\n",
    "        \n",
    "        masked_input = x\n",
    "        for l,layer in enumerate(self.QRNN_layers):\n",
    "            masked_input,h,c = layer(masked_input,h,c)\n",
    "        dense_input = h.view([self.batch_size,-1])\n",
    "        logits = self.dense(dense_input)\n",
    "        prediction = torch.sigmoid(logits)\n",
    "        target = target.view([-1,1])\n",
    "        correct_pred = torch.eq(torch.round(prediction).type(target.type()),target)\n",
    "        accuracy = torch.sum(correct_pred)\n",
    "        return prediction, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7245
    },
    "colab_type": "code",
    "id": "z9_ruCGnbcpK",
    "outputId": "a13f8791-385f-4225-b5af-4ce4c768ed9c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 \tIteration: 0 \tTrain Loss: 0.694 \tTrain Accuracy: 40.00\n",
      "Val Loss: 0.697 \tVal Acc: 49.840\n",
      "Epoch: 0/10 \tIteration: 10 \tTrain Loss: 0.706 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 20 \tTrain Loss: 0.708 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 30 \tTrain Loss: 0.676 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 40 \tTrain Loss: 0.719 \tTrain Accuracy: 38.00\n",
      "Epoch: 0/10 \tIteration: 50 \tTrain Loss: 0.687 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 60 \tTrain Loss: 0.743 \tTrain Accuracy: 46.00\n",
      "Epoch: 0/10 \tIteration: 70 \tTrain Loss: 0.619 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 80 \tTrain Loss: 0.654 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 90 \tTrain Loss: 0.583 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 100 \tTrain Loss: 0.616 \tTrain Accuracy: 70.00\n",
      "Epoch: 0/10 \tIteration: 110 \tTrain Loss: 0.530 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 120 \tTrain Loss: 0.645 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 130 \tTrain Loss: 0.543 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 140 \tTrain Loss: 0.523 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 150 \tTrain Loss: 0.580 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 160 \tTrain Loss: 0.470 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 170 \tTrain Loss: 0.368 \tTrain Accuracy: 82.00\n",
      "Epoch: 0/10 \tIteration: 180 \tTrain Loss: 0.378 \tTrain Accuracy: 82.00\n",
      "Epoch: 0/10 \tIteration: 190 \tTrain Loss: 0.504 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 200 \tTrain Loss: 0.563 \tTrain Accuracy: 76.00\n",
      "Epoch: 0/10 \tIteration: 210 \tTrain Loss: 0.477 \tTrain Accuracy: 80.00\n",
      "Epoch: 0/10 \tIteration: 220 \tTrain Loss: 0.498 \tTrain Accuracy: 76.00\n",
      "Epoch: 0/10 \tIteration: 230 \tTrain Loss: 0.361 \tTrain Accuracy: 90.00\n",
      "Epoch: 0/10 \tIteration: 240 \tTrain Loss: 0.493 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 250 \tTrain Loss: 0.563 \tTrain Accuracy: 70.00\n",
      "Epoch: 0/10 \tIteration: 260 \tTrain Loss: 0.393 \tTrain Accuracy: 82.00\n",
      "Epoch: 0/10 \tIteration: 270 \tTrain Loss: 0.330 \tTrain Accuracy: 88.00\n",
      "Epoch: 0/10 \tIteration: 280 \tTrain Loss: 0.542 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 290 \tTrain Loss: 0.419 \tTrain Accuracy: 80.00\n",
      "Epoch: 0/10 \tIteration: 300 \tTrain Loss: 0.415 \tTrain Accuracy: 82.00\n",
      "Epoch: 0/10 \tIteration: 310 \tTrain Loss: 0.407 \tTrain Accuracy: 82.00\n",
      "Epoch: 0/10 \tIteration: 320 \tTrain Loss: 0.417 \tTrain Accuracy: 80.00\n",
      "Epoch: 0/10 \tIteration: 330 \tTrain Loss: 0.349 \tTrain Accuracy: 86.00\n",
      "Epoch: 0/10 \tIteration: 340 \tTrain Loss: 0.623 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 350 \tTrain Loss: 0.306 \tTrain Accuracy: 86.00\n",
      "Epoch: 0/10 \tIteration: 360 \tTrain Loss: 0.256 \tTrain Accuracy: 88.00\n",
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      "Epoch: 6/10 \tIteration: 2750 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 6/10 \tIteration: 2760 \tTrain Loss: 0.006 \tTrain Accuracy: 100.00\n",
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      "Epoch: 6/10 \tIteration: 2790 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Val Loss: 0.881 \tVal Acc: 82.520\n",
      "Time to train epoch: 34.31701683998108 s\n",
      "Epoch: 7/10 \tIteration: 2800 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
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      "Epoch: 7/10 \tIteration: 3160 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 7/10 \tIteration: 3170 \tTrain Loss: 0.002 \tTrain Accuracy: 100.00\n",
      "Epoch: 7/10 \tIteration: 3180 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 7/10 \tIteration: 3190 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Val Loss: 0.991 \tVal Acc: 81.440\n",
      "Time to train epoch: 34.32883858680725 s\n",
      "Epoch: 8/10 \tIteration: 3200 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3210 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
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      "Epoch: 8/10 \tIteration: 3270 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3280 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3290 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
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      "Epoch: 8/10 \tIteration: 3370 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
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      "Epoch: 8/10 \tIteration: 3390 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
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      "Epoch: 8/10 \tIteration: 3440 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3450 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3460 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3470 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3480 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3490 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3500 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3510 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3520 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3530 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3540 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3550 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3560 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3570 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3580 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3590 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Val Loss: 0.961 \tVal Acc: 82.880\n",
      "Time to train epoch: 34.31767559051514 s\n",
      "Epoch: 9/10 \tIteration: 3600 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3610 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3620 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3630 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3640 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3650 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
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      "Epoch: 9/10 \tIteration: 3680 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3690 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3700 \tTrain Loss: 0.001 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3710 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
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      "Epoch: 9/10 \tIteration: 3770 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3780 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3790 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3800 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3810 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3820 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3830 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3840 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3850 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3860 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3870 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3880 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3890 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3900 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3910 \tTrain Loss: 0.002 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3920 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3930 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3940 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3950 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3960 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3970 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3980 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3990 \tTrain Loss: 0.000 \tTrain Accuracy: 100.00\n",
      "Val Loss: 1.008 \tVal Acc: 82.760\n",
      "Time to train epoch: 34.32731509208679 s\n",
      "Test Loss: 1.109 \tTest Acc: 81.920\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "epochs = 10\n",
    "n_filters = 256\n",
    "kernel_size = 2\n",
    "layers= 2\n",
    "learning_rate = 0.001\n",
    "vocab_size = len(vocab_to_int)+1\n",
    "embed_dims = 300\n",
    "seq_len = 200\n",
    "dropout = 0.3\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = QRNN(vocab_size,embed_dims,n_filters,kernel_size,batch_size,seq_len,layers,device,dropout).to(device)\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
    "counter = 0\n",
    "QRNN_acc = []\n",
    "QRNN_valacc = []\n",
    "\n",
    "model.train()\n",
    "for e in range(epochs):\n",
    "  start_time = time.time()\n",
    "  for inputs, labels in train_loader:\n",
    "    \n",
    "    inputs, labels = inputs.cuda(), labels.cuda()\n",
    "    model.zero_grad()\n",
    "    logits, accuracy = model(inputs,labels)\n",
    "    loss = criterion(logits,labels.float())\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "    optimizer.step()\n",
    "    if counter%10==0:\n",
    "        print(\"Epoch: {}/{}\".format(e,epochs),\n",
    "                     \"\\tIteration: {}\".format(counter),\n",
    "                     \"\\tTrain Loss: {:.3f}\".format(loss.item()),\n",
    "                     \"\\tTrain Accuracy: {:.2f}\".format(accuracy.item()*100/batch_size))\n",
    "        QRNN_acc.append(accuracy.item()*100/batch_size)\n",
    "    if counter%399==0:\n",
    "            with torch.no_grad():\n",
    "                model.eval()\n",
    "                val_acc = []\n",
    "                val_loss = []\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs_val, labels_val = inputs.cuda(), labels.cuda()\n",
    "                    logits_val,accuracy_val = model(inputs_val,labels_val)\n",
    "                    loss_val = criterion(logits_val,labels_val.float())\n",
    "                    val_acc.append(accuracy_val.item()*100/batch_size)\n",
    "                    val_loss.append(loss_val.item())\n",
    "                print(\"Val Loss: {:.3f}\".format(np.mean(val_loss)), \"\\tVal Acc: {:.3f}\".format(np.mean(val_acc)))\n",
    "                QRNN_valacc.append(np.mean(val_acc))\n",
    "                model.train()\n",
    "    counter += 1\n",
    "  print(\"Time to train epoch: {0} s\".format(time.time()-start_time))  \n",
    "with torch.no_grad():\n",
    "  model.eval()\n",
    "  test_acc = []\n",
    "  test_loss = []\n",
    "  for inputs, labels in test_loader:\n",
    "    input_test, labels_test = inputs.cuda(), labels.cuda()\n",
    "    logits_test,accuracy_test = model(input_test,labels_test)\n",
    "    loss_test = criterion(logits_test,labels_test.float())\n",
    "    test_acc.append(accuracy_test.item()*100/batch_size)\n",
    "    test_loss.append(loss_test.item())\n",
    "  print(\"Test Loss: {:.3f}\".format(np.mean(test_loss)), \"\\tTest Acc: {:.3f}\".format(np.mean(test_acc)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xRa43jU1dwO4"
   },
   "source": [
    "## Basic GRU Cell (Unidirectional)\n",
    "The Gated Recurrent Unit is a recurrent neural network with a gating mechansism introduced in https://arxiv.org/abs/1406.1078. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7245
    },
    "colab_type": "code",
    "id": "BFNVbOehhQHU",
    "outputId": "4d814ef6-560d-42a4-e61c-c0e8edd36c47"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 \tIteration: 0 \tTrain Loss: 0.691 \tTrain Accuracy: 56.00\n",
      "Val Loss: 1.335 \tVal Acc: 49.960\n",
      "Epoch: 0/10 \tIteration: 10 \tTrain Loss: 0.711 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 20 \tTrain Loss: 0.761 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 30 \tTrain Loss: 0.738 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 40 \tTrain Loss: 0.872 \tTrain Accuracy: 42.00\n",
      "Epoch: 0/10 \tIteration: 50 \tTrain Loss: 0.686 \tTrain Accuracy: 70.00\n",
      "Epoch: 0/10 \tIteration: 60 \tTrain Loss: 0.669 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 70 \tTrain Loss: 0.792 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 80 \tTrain Loss: 0.742 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 90 \tTrain Loss: 0.772 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 100 \tTrain Loss: 0.798 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 110 \tTrain Loss: 0.821 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 120 \tTrain Loss: 0.783 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 130 \tTrain Loss: 0.774 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 140 \tTrain Loss: 0.832 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 150 \tTrain Loss: 0.843 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 160 \tTrain Loss: 0.893 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 170 \tTrain Loss: 0.745 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 180 \tTrain Loss: 0.892 \tTrain Accuracy: 48.00\n",
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      "Epoch: 5/10 \tIteration: 2340 \tTrain Loss: 0.556 \tTrain Accuracy: 86.00\n",
      "Epoch: 5/10 \tIteration: 2350 \tTrain Loss: 0.305 \tTrain Accuracy: 86.00\n",
      "Epoch: 5/10 \tIteration: 2360 \tTrain Loss: 1.139 \tTrain Accuracy: 74.00\n",
      "Epoch: 5/10 \tIteration: 2370 \tTrain Loss: 1.377 \tTrain Accuracy: 70.00\n",
      "Epoch: 5/10 \tIteration: 2380 \tTrain Loss: 0.431 \tTrain Accuracy: 80.00\n",
      "Epoch: 5/10 \tIteration: 2390 \tTrain Loss: 0.505 \tTrain Accuracy: 82.00\n",
      "Val Loss: 0.896 \tVal Acc: 77.680\n",
      "Time to train epoch: 19.532960891723633 s\n",
      "Epoch: 6/10 \tIteration: 2400 \tTrain Loss: 0.250 \tTrain Accuracy: 90.00\n",
      "Epoch: 6/10 \tIteration: 2410 \tTrain Loss: 0.364 \tTrain Accuracy: 84.00\n",
      "Epoch: 6/10 \tIteration: 2420 \tTrain Loss: 0.472 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2430 \tTrain Loss: 0.343 \tTrain Accuracy: 90.00\n",
      "Epoch: 6/10 \tIteration: 2440 \tTrain Loss: 0.329 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2450 \tTrain Loss: 0.268 \tTrain Accuracy: 92.00\n",
      "Epoch: 6/10 \tIteration: 2460 \tTrain Loss: 0.493 \tTrain Accuracy: 84.00\n",
      "Epoch: 6/10 \tIteration: 2470 \tTrain Loss: 0.356 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2480 \tTrain Loss: 0.490 \tTrain Accuracy: 82.00\n",
      "Epoch: 6/10 \tIteration: 2490 \tTrain Loss: 0.447 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2500 \tTrain Loss: 0.421 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2510 \tTrain Loss: 0.598 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2520 \tTrain Loss: 0.413 \tTrain Accuracy: 82.00\n",
      "Epoch: 6/10 \tIteration: 2530 \tTrain Loss: 0.523 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2540 \tTrain Loss: 0.387 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2550 \tTrain Loss: 0.573 \tTrain Accuracy: 82.00\n",
      "Epoch: 6/10 \tIteration: 2560 \tTrain Loss: 0.361 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2570 \tTrain Loss: 0.279 \tTrain Accuracy: 94.00\n",
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      "Epoch: 6/10 \tIteration: 2590 \tTrain Loss: 0.486 \tTrain Accuracy: 90.00\n",
      "Epoch: 6/10 \tIteration: 2600 \tTrain Loss: 0.461 \tTrain Accuracy: 84.00\n",
      "Epoch: 6/10 \tIteration: 2610 \tTrain Loss: 0.550 \tTrain Accuracy: 84.00\n",
      "Epoch: 6/10 \tIteration: 2620 \tTrain Loss: 0.559 \tTrain Accuracy: 92.00\n",
      "Epoch: 6/10 \tIteration: 2630 \tTrain Loss: 0.377 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2640 \tTrain Loss: 0.686 \tTrain Accuracy: 84.00\n",
      "Epoch: 6/10 \tIteration: 2650 \tTrain Loss: 0.632 \tTrain Accuracy: 80.00\n",
      "Epoch: 6/10 \tIteration: 2660 \tTrain Loss: 0.527 \tTrain Accuracy: 82.00\n",
      "Epoch: 6/10 \tIteration: 2670 \tTrain Loss: 0.792 \tTrain Accuracy: 70.00\n",
      "Epoch: 6/10 \tIteration: 2680 \tTrain Loss: 0.663 \tTrain Accuracy: 78.00\n",
      "Epoch: 6/10 \tIteration: 2690 \tTrain Loss: 0.533 \tTrain Accuracy: 84.00\n",
      "Epoch: 6/10 \tIteration: 2700 \tTrain Loss: 0.378 \tTrain Accuracy: 92.00\n",
      "Epoch: 6/10 \tIteration: 2710 \tTrain Loss: 0.426 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2720 \tTrain Loss: 0.254 \tTrain Accuracy: 90.00\n",
      "Epoch: 6/10 \tIteration: 2730 \tTrain Loss: 0.243 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2740 \tTrain Loss: 0.364 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2750 \tTrain Loss: 0.678 \tTrain Accuracy: 76.00\n",
      "Epoch: 6/10 \tIteration: 2760 \tTrain Loss: 0.411 \tTrain Accuracy: 88.00\n",
      "Epoch: 6/10 \tIteration: 2770 \tTrain Loss: 0.801 \tTrain Accuracy: 80.00\n",
      "Epoch: 6/10 \tIteration: 2780 \tTrain Loss: 0.473 \tTrain Accuracy: 86.00\n",
      "Epoch: 6/10 \tIteration: 2790 \tTrain Loss: 0.373 \tTrain Accuracy: 88.00\n",
      "Val Loss: 1.004 \tVal Acc: 78.920\n",
      "Time to train epoch: 19.51736092567444 s\n",
      "Epoch: 7/10 \tIteration: 2800 \tTrain Loss: 0.584 \tTrain Accuracy: 90.00\n",
      "Epoch: 7/10 \tIteration: 2810 \tTrain Loss: 0.565 \tTrain Accuracy: 86.00\n",
      "Epoch: 7/10 \tIteration: 2820 \tTrain Loss: 0.464 \tTrain Accuracy: 84.00\n",
      "Epoch: 7/10 \tIteration: 2830 \tTrain Loss: 0.393 \tTrain Accuracy: 88.00\n",
      "Epoch: 7/10 \tIteration: 2840 \tTrain Loss: 0.107 \tTrain Accuracy: 98.00\n",
      "Epoch: 7/10 \tIteration: 2850 \tTrain Loss: 0.346 \tTrain Accuracy: 86.00\n",
      "Epoch: 7/10 \tIteration: 2860 \tTrain Loss: 0.700 \tTrain Accuracy: 84.00\n",
      "Epoch: 7/10 \tIteration: 2870 \tTrain Loss: 0.261 \tTrain Accuracy: 90.00\n",
      "Epoch: 7/10 \tIteration: 2880 \tTrain Loss: 0.399 \tTrain Accuracy: 84.00\n",
      "Epoch: 7/10 \tIteration: 2890 \tTrain Loss: 0.655 \tTrain Accuracy: 84.00\n",
      "Epoch: 7/10 \tIteration: 2900 \tTrain Loss: 0.968 \tTrain Accuracy: 86.00\n",
      "Epoch: 7/10 \tIteration: 2910 \tTrain Loss: 1.160 \tTrain Accuracy: 74.00\n",
      "Epoch: 7/10 \tIteration: 2920 \tTrain Loss: 0.092 \tTrain Accuracy: 96.00\n",
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      "Epoch: 7/10 \tIteration: 2950 \tTrain Loss: 0.608 \tTrain Accuracy: 88.00\n",
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      "Epoch: 7/10 \tIteration: 2980 \tTrain Loss: 0.644 \tTrain Accuracy: 78.00\n",
      "Epoch: 7/10 \tIteration: 2990 \tTrain Loss: 0.297 \tTrain Accuracy: 92.00\n",
      "Epoch: 7/10 \tIteration: 3000 \tTrain Loss: 0.329 \tTrain Accuracy: 90.00\n",
      "Epoch: 7/10 \tIteration: 3010 \tTrain Loss: 0.904 \tTrain Accuracy: 82.00\n",
      "Epoch: 7/10 \tIteration: 3020 \tTrain Loss: 0.430 \tTrain Accuracy: 92.00\n",
      "Epoch: 7/10 \tIteration: 3030 \tTrain Loss: 0.912 \tTrain Accuracy: 78.00\n",
      "Epoch: 7/10 \tIteration: 3040 \tTrain Loss: 0.414 \tTrain Accuracy: 94.00\n",
      "Epoch: 7/10 \tIteration: 3050 \tTrain Loss: 0.358 \tTrain Accuracy: 88.00\n",
      "Epoch: 7/10 \tIteration: 3060 \tTrain Loss: 0.368 \tTrain Accuracy: 88.00\n",
      "Epoch: 7/10 \tIteration: 3070 \tTrain Loss: 0.188 \tTrain Accuracy: 90.00\n",
      "Epoch: 7/10 \tIteration: 3080 \tTrain Loss: 0.174 \tTrain Accuracy: 94.00\n",
      "Epoch: 7/10 \tIteration: 3090 \tTrain Loss: 0.143 \tTrain Accuracy: 96.00\n",
      "Epoch: 7/10 \tIteration: 3100 \tTrain Loss: 0.247 \tTrain Accuracy: 90.00\n",
      "Epoch: 7/10 \tIteration: 3110 \tTrain Loss: 0.750 \tTrain Accuracy: 82.00\n",
      "Epoch: 7/10 \tIteration: 3120 \tTrain Loss: 0.492 \tTrain Accuracy: 90.00\n",
      "Epoch: 7/10 \tIteration: 3130 \tTrain Loss: 0.402 \tTrain Accuracy: 86.00\n",
      "Epoch: 7/10 \tIteration: 3140 \tTrain Loss: 0.407 \tTrain Accuracy: 88.00\n",
      "Epoch: 7/10 \tIteration: 3150 \tTrain Loss: 0.798 \tTrain Accuracy: 82.00\n",
      "Epoch: 7/10 \tIteration: 3160 \tTrain Loss: 0.121 \tTrain Accuracy: 92.00\n",
      "Epoch: 7/10 \tIteration: 3170 \tTrain Loss: 0.156 \tTrain Accuracy: 92.00\n",
      "Epoch: 7/10 \tIteration: 3180 \tTrain Loss: 0.445 \tTrain Accuracy: 82.00\n",
      "Epoch: 7/10 \tIteration: 3190 \tTrain Loss: 0.555 \tTrain Accuracy: 88.00\n",
      "Val Loss: 1.047 \tVal Acc: 78.440\n",
      "Time to train epoch: 19.510862350463867 s\n",
      "Epoch: 8/10 \tIteration: 3200 \tTrain Loss: 0.378 \tTrain Accuracy: 92.00\n",
      "Epoch: 8/10 \tIteration: 3210 \tTrain Loss: 0.533 \tTrain Accuracy: 86.00\n",
      "Epoch: 8/10 \tIteration: 3220 \tTrain Loss: 0.792 \tTrain Accuracy: 80.00\n",
      "Epoch: 8/10 \tIteration: 3230 \tTrain Loss: 0.449 \tTrain Accuracy: 86.00\n",
      "Epoch: 8/10 \tIteration: 3240 \tTrain Loss: 0.080 \tTrain Accuracy: 94.00\n",
      "Epoch: 8/10 \tIteration: 3250 \tTrain Loss: 0.415 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3260 \tTrain Loss: 0.505 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3270 \tTrain Loss: 0.192 \tTrain Accuracy: 92.00\n",
      "Epoch: 8/10 \tIteration: 3280 \tTrain Loss: 0.784 \tTrain Accuracy: 82.00\n",
      "Epoch: 8/10 \tIteration: 3290 \tTrain Loss: 0.156 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3300 \tTrain Loss: 0.248 \tTrain Accuracy: 94.00\n",
      "Epoch: 8/10 \tIteration: 3310 \tTrain Loss: 0.444 \tTrain Accuracy: 88.00\n",
      "Epoch: 8/10 \tIteration: 3320 \tTrain Loss: 0.274 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3330 \tTrain Loss: 0.231 \tTrain Accuracy: 92.00\n",
      "Epoch: 8/10 \tIteration: 3340 \tTrain Loss: 0.460 \tTrain Accuracy: 82.00\n",
      "Epoch: 8/10 \tIteration: 3350 \tTrain Loss: 0.277 \tTrain Accuracy: 86.00\n",
      "Epoch: 8/10 \tIteration: 3360 \tTrain Loss: 0.367 \tTrain Accuracy: 88.00\n",
      "Epoch: 8/10 \tIteration: 3370 \tTrain Loss: 0.190 \tTrain Accuracy: 94.00\n",
      "Epoch: 8/10 \tIteration: 3380 \tTrain Loss: 0.458 \tTrain Accuracy: 82.00\n",
      "Epoch: 8/10 \tIteration: 3390 \tTrain Loss: 1.028 \tTrain Accuracy: 82.00\n",
      "Epoch: 8/10 \tIteration: 3400 \tTrain Loss: 0.585 \tTrain Accuracy: 88.00\n",
      "Epoch: 8/10 \tIteration: 3410 \tTrain Loss: 0.392 \tTrain Accuracy: 92.00\n",
      "Epoch: 8/10 \tIteration: 3420 \tTrain Loss: 0.444 \tTrain Accuracy: 86.00\n",
      "Epoch: 8/10 \tIteration: 3430 \tTrain Loss: 0.495 \tTrain Accuracy: 86.00\n",
      "Epoch: 8/10 \tIteration: 3440 \tTrain Loss: 0.395 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3450 \tTrain Loss: 0.891 \tTrain Accuracy: 84.00\n",
      "Epoch: 8/10 \tIteration: 3460 \tTrain Loss: 0.780 \tTrain Accuracy: 78.00\n",
      "Epoch: 8/10 \tIteration: 3470 \tTrain Loss: 1.395 \tTrain Accuracy: 76.00\n",
      "Epoch: 8/10 \tIteration: 3480 \tTrain Loss: 0.153 \tTrain Accuracy: 92.00\n",
      "Epoch: 8/10 \tIteration: 3490 \tTrain Loss: 0.131 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3500 \tTrain Loss: 0.626 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3510 \tTrain Loss: 0.196 \tTrain Accuracy: 94.00\n",
      "Epoch: 8/10 \tIteration: 3520 \tTrain Loss: 0.313 \tTrain Accuracy: 92.00\n",
      "Epoch: 8/10 \tIteration: 3530 \tTrain Loss: 0.778 \tTrain Accuracy: 84.00\n",
      "Epoch: 8/10 \tIteration: 3540 \tTrain Loss: 0.570 \tTrain Accuracy: 88.00\n",
      "Epoch: 8/10 \tIteration: 3550 \tTrain Loss: 0.286 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3560 \tTrain Loss: 0.530 \tTrain Accuracy: 84.00\n",
      "Epoch: 8/10 \tIteration: 3570 \tTrain Loss: 0.373 \tTrain Accuracy: 90.00\n",
      "Epoch: 8/10 \tIteration: 3580 \tTrain Loss: 0.502 \tTrain Accuracy: 82.00\n",
      "Epoch: 8/10 \tIteration: 3590 \tTrain Loss: 0.147 \tTrain Accuracy: 92.00\n",
      "Val Loss: 1.133 \tVal Acc: 78.560\n",
      "Time to train epoch: 19.498099088668823 s\n",
      "Epoch: 9/10 \tIteration: 3600 \tTrain Loss: 0.541 \tTrain Accuracy: 88.00\n",
      "Epoch: 9/10 \tIteration: 3610 \tTrain Loss: 0.228 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3620 \tTrain Loss: 0.345 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3630 \tTrain Loss: 0.337 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3640 \tTrain Loss: 0.711 \tTrain Accuracy: 80.00\n",
      "Epoch: 9/10 \tIteration: 3650 \tTrain Loss: 0.205 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3660 \tTrain Loss: 0.700 \tTrain Accuracy: 86.00\n",
      "Epoch: 9/10 \tIteration: 3670 \tTrain Loss: 0.120 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3680 \tTrain Loss: 0.481 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3690 \tTrain Loss: 0.308 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3700 \tTrain Loss: 0.442 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3710 \tTrain Loss: 1.094 \tTrain Accuracy: 80.00\n",
      "Epoch: 9/10 \tIteration: 3720 \tTrain Loss: 0.575 \tTrain Accuracy: 84.00\n",
      "Epoch: 9/10 \tIteration: 3730 \tTrain Loss: 0.242 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3740 \tTrain Loss: 0.142 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3750 \tTrain Loss: 0.113 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3760 \tTrain Loss: 0.421 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3770 \tTrain Loss: 0.231 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3780 \tTrain Loss: 0.216 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3790 \tTrain Loss: 0.419 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3800 \tTrain Loss: 0.187 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3810 \tTrain Loss: 0.314 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3820 \tTrain Loss: 0.409 \tTrain Accuracy: 88.00\n",
      "Epoch: 9/10 \tIteration: 3830 \tTrain Loss: 0.259 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3840 \tTrain Loss: 0.246 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3850 \tTrain Loss: 0.876 \tTrain Accuracy: 84.00\n",
      "Epoch: 9/10 \tIteration: 3860 \tTrain Loss: 0.173 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3870 \tTrain Loss: 0.225 \tTrain Accuracy: 88.00\n",
      "Epoch: 9/10 \tIteration: 3880 \tTrain Loss: 0.233 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3890 \tTrain Loss: 0.123 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3900 \tTrain Loss: 0.192 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3910 \tTrain Loss: 0.387 \tTrain Accuracy: 88.00\n",
      "Epoch: 9/10 \tIteration: 3920 \tTrain Loss: 0.224 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3930 \tTrain Loss: 0.139 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3940 \tTrain Loss: 0.528 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3950 \tTrain Loss: 0.758 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3960 \tTrain Loss: 0.216 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3970 \tTrain Loss: 0.358 \tTrain Accuracy: 92.00\n",
      "Epoch: 9/10 \tIteration: 3980 \tTrain Loss: 0.629 \tTrain Accuracy: 90.00\n",
      "Epoch: 9/10 \tIteration: 3990 \tTrain Loss: 0.434 \tTrain Accuracy: 86.00\n",
      "Val Loss: 1.266 \tVal Acc: 78.800\n",
      "Time to train epoch: 19.478496551513672 s\n",
      "Test Loss: 1.284 \tTest Acc: 78.960\n"
     ]
    }
   ],
   "source": [
    "class UniGRU(nn.Module):\n",
    "    def __init__(self,vocab_size,embed_size,num_units,output_size,dropout,seq_len,batch_size):\n",
    "        super(UniGRU, self).__init__()\n",
    "        self.embedding = torch.nn.Embedding(vocab_size,embed_size)\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "        self.rnn = torch.nn.GRU(embed_size,num_units,num_layers=1,bidirectional=False)\n",
    "        self.dense = torch.nn.Linear(seq_len*num_units,output_size)\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "    def forward(self,x,target):\n",
    "        embed_input = self.embedding(x)\n",
    "        embed_input = self.dropout(embed_input)\n",
    "        output, hidden = self.rnn(embed_input)\n",
    "        logits = self.dense(output.view([self.batch_size,-1]))\n",
    "        prediction = torch.sigmoid(logits)\n",
    "        target = target.view([-1,1])\n",
    "        correct_pred = torch.eq(torch.round(prediction).type(target.type()),target)\n",
    "        accuracy = torch.sum(correct_pred)\n",
    "        return prediction, accuracy\n",
    "\n",
    "      \n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "epochs = 10\n",
    "batch_size = 50\n",
    "num_units = 256\n",
    "learning_rate = 0.001\n",
    "embed_dims = 300\n",
    "seq_len = 200\n",
    "dropout = 0.5\n",
    "vocab_size = len(vocab_to_int)+1\n",
    "model = UniGRU(vocab_size,embed_dims,num_units,1,dropout,seq_len,batch_size).to(device)\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
    "counter = 0\n",
    "GRU_acc = []\n",
    "GRU_valacc = []\n",
    "model.train()\n",
    "for e in range(epochs):\n",
    "  start_time = time.time()\n",
    "  for inputs, labels in train_loader:\n",
    "    \n",
    "    inputs, labels = inputs.cuda(), labels.cuda()\n",
    "    model.zero_grad()\n",
    "    logits, accuracy = model(inputs,labels)\n",
    "    loss = criterion(logits,labels.float())\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "    optimizer.step()\n",
    "    if counter%10==0:\n",
    "        print(\"Epoch: {}/{}\".format(e,epochs),\n",
    "                     \"\\tIteration: {}\".format(counter),\n",
    "                     \"\\tTrain Loss: {:.3f}\".format(loss.item()),\n",
    "                     \"\\tTrain Accuracy: {:.2f}\".format(accuracy.item()*100/batch_size))\n",
    "        GRU_acc.append(accuracy.item()*100/batch_size)\n",
    "    if counter%399==0:\n",
    "            with torch.no_grad():\n",
    "                model.eval()\n",
    "                val_acc = []\n",
    "                val_loss = []\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs_val, labels_val = inputs.cuda(), labels.cuda()\n",
    "                    logits_val,accuracy_val = model(inputs_val,labels_val)\n",
    "                    loss_val = criterion(logits_val,labels_val.float())\n",
    "                    val_acc.append(accuracy_val.item()*100/batch_size)\n",
    "                    val_loss.append(loss_val.item())\n",
    "                print(\"Val Loss: {:.3f}\".format(np.mean(val_loss)), \"\\tVal Acc: {:.3f}\".format(np.mean(val_acc)))\n",
    "                GRU_valacc.append(np.mean(val_acc))\n",
    "                model.train()\n",
    "    counter += 1\n",
    "  print(\"Time to train epoch: {0} s\".format(time.time()-start_time))  \n",
    "with torch.no_grad():\n",
    "  model.eval()\n",
    "  test_acc = []\n",
    "  test_loss = []\n",
    "  for inputs, labels in test_loader:\n",
    "    input_test, labels_test = inputs.cuda(), labels.cuda()\n",
    "    logits_test,accuracy_test = model(input_test,labels_test)\n",
    "    loss_test = criterion(logits_test,labels_test.float())\n",
    "    test_acc.append(accuracy_test.item()*100/batch_size)\n",
    "    test_loss.append(loss_test.item())\n",
    "print(\"Test Loss: {:.3f}\".format(np.mean(test_loss)), \"\\tTest Acc: {:.3f}\".format(np.mean(test_acc)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kjC-mt2piL6N"
   },
   "source": [
    "## Basic LSTM Cell (Unidirectional)\n",
    "A basic 1 layer unidirectional LSTM with 256 units. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7245
    },
    "colab_type": "code",
    "id": "SBuS33pyiLsr",
    "outputId": "f3bb8ec4-924c-4ed6-c882-ce10128194c2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 \tIteration: 0 \tTrain Loss: 0.696 \tTrain Accuracy: 60.00\n",
      "Val Loss: 0.750 \tVal Acc: 50.080\n",
      "Epoch: 0/10 \tIteration: 10 \tTrain Loss: 0.713 \tTrain Accuracy: 42.00\n",
      "Epoch: 0/10 \tIteration: 20 \tTrain Loss: 0.657 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 30 \tTrain Loss: 0.719 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 40 \tTrain Loss: 0.740 \tTrain Accuracy: 46.00\n",
      "Epoch: 0/10 \tIteration: 50 \tTrain Loss: 0.677 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 60 \tTrain Loss: 0.719 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 70 \tTrain Loss: 0.698 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 80 \tTrain Loss: 0.664 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 90 \tTrain Loss: 0.716 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 100 \tTrain Loss: 0.650 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 110 \tTrain Loss: 0.733 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 120 \tTrain Loss: 0.632 \tTrain Accuracy: 66.00\n",
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      "Epoch: 0/10 \tIteration: 140 \tTrain Loss: 0.715 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 150 \tTrain Loss: 0.626 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 160 \tTrain Loss: 0.657 \tTrain Accuracy: 62.00\n",
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      "Epoch: 0/10 \tIteration: 270 \tTrain Loss: 0.543 \tTrain Accuracy: 66.00\n",
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      "Epoch: 0/10 \tIteration: 300 \tTrain Loss: 0.492 \tTrain Accuracy: 80.00\n",
      "Epoch: 0/10 \tIteration: 310 \tTrain Loss: 0.514 \tTrain Accuracy: 80.00\n",
      "Epoch: 0/10 \tIteration: 320 \tTrain Loss: 0.594 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 330 \tTrain Loss: 0.472 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 340 \tTrain Loss: 0.614 \tTrain Accuracy: 70.00\n",
      "Epoch: 0/10 \tIteration: 350 \tTrain Loss: 0.480 \tTrain Accuracy: 76.00\n",
      "Epoch: 0/10 \tIteration: 360 \tTrain Loss: 0.547 \tTrain Accuracy: 82.00\n",
      "Epoch: 0/10 \tIteration: 370 \tTrain Loss: 0.514 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 380 \tTrain Loss: 0.513 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 390 \tTrain Loss: 0.696 \tTrain Accuracy: 62.00\n",
      "Val Loss: 0.486 \tVal Acc: 76.960\n",
      "Time to train epoch: 21.19278883934021 s\n",
      "Epoch: 1/10 \tIteration: 400 \tTrain Loss: 0.385 \tTrain Accuracy: 86.00\n",
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      "Epoch: 1/10 \tIteration: 790 \tTrain Loss: 0.284 \tTrain Accuracy: 90.00\n",
      "Val Loss: 0.469 \tVal Acc: 78.560\n",
      "Time to train epoch: 20.566849946975708 s\n",
      "Epoch: 2/10 \tIteration: 800 \tTrain Loss: 0.281 \tTrain Accuracy: 88.00\n",
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      "Epoch: 2/10 \tIteration: 1190 \tTrain Loss: 0.273 \tTrain Accuracy: 86.00\n",
      "Val Loss: 0.454 \tVal Acc: 82.080\n",
      "Time to train epoch: 20.571101427078247 s\n",
      "Epoch: 3/10 \tIteration: 1200 \tTrain Loss: 0.247 \tTrain Accuracy: 90.00\n",
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      "Epoch: 3/10 \tIteration: 1590 \tTrain Loss: 0.248 \tTrain Accuracy: 90.00\n",
      "Val Loss: 0.482 \tVal Acc: 82.840\n",
      "Time to train epoch: 20.644585371017456 s\n",
      "Epoch: 4/10 \tIteration: 1600 \tTrain Loss: 0.079 \tTrain Accuracy: 96.00\n",
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      "Epoch: 4/10 \tIteration: 1990 \tTrain Loss: 0.137 \tTrain Accuracy: 92.00\n",
      "Val Loss: 0.519 \tVal Acc: 82.080\n",
      "Time to train epoch: 20.572051286697388 s\n",
      "Epoch: 5/10 \tIteration: 2000 \tTrain Loss: 0.125 \tTrain Accuracy: 94.00\n",
      "Epoch: 5/10 \tIteration: 2010 \tTrain Loss: 0.055 \tTrain Accuracy: 100.00\n",
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      "Val Loss: 0.552 \tVal Acc: 82.000\n",
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      "Val Loss: 0.595 \tVal Acc: 82.600\n",
      "Time to train epoch: 20.638816356658936 s\n",
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      "Val Loss: 0.630 \tVal Acc: 81.800\n",
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      "Val Loss: 0.666 \tVal Acc: 82.120\n",
      "Time to train epoch: 20.593857765197754 s\n",
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      "Epoch: 9/10 \tIteration: 3990 \tTrain Loss: 0.019 \tTrain Accuracy: 100.00\n",
      "Val Loss: 0.709 \tVal Acc: 82.400\n",
      "Time to train epoch: 20.638312816619873 s\n",
      "Test Loss: 0.826 \tTest Acc: 80.080\n"
     ]
    }
   ],
   "source": [
    "class UniLSTM(nn.Module):\n",
    "    def __init__(self,vocab_size,embed_size,num_units,output_size,dropout,seq_len,batch_size):\n",
    "        super(UniLSTM, self).__init__()\n",
    "        self.embedding = torch.nn.Embedding(vocab_size,embed_size)\n",
    "        self.batch_size = batch_size\n",
    "        self.rnn = torch.nn.LSTM(embed_size,num_units,num_layers=1,bidirectional=False)\n",
    "        self.dense = torch.nn.Linear(seq_len*num_units,output_size)\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "    def forward(self,x,target):\n",
    "        embed_input = self.embedding(x)\n",
    "        embed_input = self.dropout(embed_input)\n",
    "        output, hidden = self.rnn(embed_input)\n",
    "        logits = self.dense(output.view([self.batch_size,-1]))\n",
    "        prediction = torch.sigmoid(logits)\n",
    "        target = target.view([-1,1])\n",
    "        correct_pred = torch.eq(torch.round(prediction).type(target.type()),target)\n",
    "        accuracy = torch.sum(correct_pred)\n",
    "        return prediction, accuracy\n",
    "      \n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "epochs = 10\n",
    "batch_size = 50\n",
    "num_units = 256\n",
    "learning_rate = 0.001\n",
    "embed_dims = 300\n",
    "seq_len = 200\n",
    "dropout = 0.5\n",
    "vocab_size = len(vocab_to_int)+1\n",
    "\n",
    "model = UniLSTM(vocab_size,embed_dims,num_units,1,dropout,seq_len,batch_size).to(device)\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
    "counter = 0\n",
    "UniLSTM_acc = []\n",
    "UniLSTM_valacc = []\n",
    "model.train()\n",
    "for e in range(epochs):\n",
    "  start_time = time.time()\n",
    "  for inputs, labels in train_loader:\n",
    "    \n",
    "    inputs, labels = inputs.cuda(), labels.cuda()\n",
    "    model.zero_grad()\n",
    "    logits, accuracy = model(inputs,labels)\n",
    "    loss = criterion(logits,labels.float())\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "    optimizer.step()\n",
    "    if counter%10==0:\n",
    "        print(\"Epoch: {}/{}\".format(e,epochs),\n",
    "                     \"\\tIteration: {}\".format(counter),\n",
    "                     \"\\tTrain Loss: {:.3f}\".format(loss.item()),\n",
    "                     \"\\tTrain Accuracy: {:.2f}\".format(accuracy.item()*100/batch_size))\n",
    "        UniLSTM_acc.append(accuracy.item()*100/batch_size)\n",
    "    if counter%399==0:\n",
    "            with torch.no_grad():\n",
    "                model.eval()\n",
    "                val_acc = []\n",
    "                val_loss = []\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs_val, labels_val = inputs.cuda(), labels.cuda()\n",
    "                    logits_val,accuracy_val = model(inputs_val,labels_val)\n",
    "                    loss_val = criterion(logits_val,labels_val.float())\n",
    "                    val_acc.append(accuracy_val.item()*100/batch_size)\n",
    "                    val_loss.append(loss_val.item())\n",
    "                print(\"Val Loss: {:.3f}\".format(np.mean(val_loss)), \"\\tVal Acc: {:.3f}\".format(np.mean(val_acc)))\n",
    "                UniLSTM_valacc.append(np.mean(val_acc))\n",
    "                model.train()\n",
    "    counter += 1\n",
    "  print(\"Time to train epoch: {0} s\".format(time.time()-start_time))  \n",
    "with torch.no_grad():\n",
    "  model.eval()\n",
    "  test_acc = []\n",
    "  test_loss = []\n",
    "  for inputs, labels in test_loader:\n",
    "    input_test, labels_test = inputs.cuda(), labels.cuda()\n",
    "    logits_test,accuracy_test = model(input_test,labels_test)\n",
    "    loss_test = criterion(logits_test,labels_test.float())\n",
    "    test_acc.append(accuracy_test.item()*100/batch_size)\n",
    "    test_loss.append(loss_test.item())\n",
    "print(\"Test Loss: {:.3f}\".format(np.mean(test_loss)), \"\\tTest Acc: {:.3f}\".format(np.mean(test_acc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LIwjoCszikPX"
   },
   "source": [
    "## Basic LSTM Cell (Bidirectional)\n",
    "A bidirectional LSTM with 256 units in each direction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7245
    },
    "colab_type": "code",
    "id": "9Z9HBkJMij-0",
    "outputId": "0da537a2-1e59-49cb-8aa9-42a2abf52faf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 \tIteration: 0 \tTrain Loss: 0.698 \tTrain Accuracy: 46.00\n",
      "Val Loss: 0.897 \tVal Acc: 49.960\n",
      "Epoch: 0/10 \tIteration: 10 \tTrain Loss: 0.693 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 20 \tTrain Loss: 0.699 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 30 \tTrain Loss: 0.719 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 40 \tTrain Loss: 0.735 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 50 \tTrain Loss: 0.681 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 60 \tTrain Loss: 0.653 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 70 \tTrain Loss: 0.666 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 80 \tTrain Loss: 0.683 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 90 \tTrain Loss: 0.732 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 100 \tTrain Loss: 0.721 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 110 \tTrain Loss: 0.795 \tTrain Accuracy: 46.00\n",
      "Epoch: 0/10 \tIteration: 120 \tTrain Loss: 0.749 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 130 \tTrain Loss: 0.716 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 140 \tTrain Loss: 0.726 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 150 \tTrain Loss: 0.631 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 160 \tTrain Loss: 0.694 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 170 \tTrain Loss: 0.778 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 180 \tTrain Loss: 0.712 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 190 \tTrain Loss: 0.734 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 200 \tTrain Loss: 0.996 \tTrain Accuracy: 44.00\n",
      "Epoch: 0/10 \tIteration: 210 \tTrain Loss: 0.679 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 220 \tTrain Loss: 0.737 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 230 \tTrain Loss: 0.734 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 240 \tTrain Loss: 0.838 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 250 \tTrain Loss: 0.552 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 260 \tTrain Loss: 0.743 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 270 \tTrain Loss: 0.730 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 280 \tTrain Loss: 0.567 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 290 \tTrain Loss: 0.723 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 300 \tTrain Loss: 0.680 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 310 \tTrain Loss: 0.582 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 320 \tTrain Loss: 0.533 \tTrain Accuracy: 78.00\n",
      "Epoch: 0/10 \tIteration: 330 \tTrain Loss: 0.747 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 340 \tTrain Loss: 0.808 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 350 \tTrain Loss: 0.696 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 360 \tTrain Loss: 0.491 \tTrain Accuracy: 76.00\n",
      "Epoch: 0/10 \tIteration: 370 \tTrain Loss: 0.554 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 380 \tTrain Loss: 0.734 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 390 \tTrain Loss: 0.536 \tTrain Accuracy: 74.00\n",
      "Val Loss: 0.601 \tVal Acc: 72.040\n",
      "Time to train epoch: 30.242960453033447 s\n",
      "Epoch: 1/10 \tIteration: 400 \tTrain Loss: 0.353 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 410 \tTrain Loss: 0.330 \tTrain Accuracy: 92.00\n",
      "Epoch: 1/10 \tIteration: 420 \tTrain Loss: 0.356 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 430 \tTrain Loss: 0.448 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 440 \tTrain Loss: 0.357 \tTrain Accuracy: 90.00\n",
      "Epoch: 1/10 \tIteration: 450 \tTrain Loss: 0.451 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 460 \tTrain Loss: 0.312 \tTrain Accuracy: 88.00\n",
      "Epoch: 1/10 \tIteration: 470 \tTrain Loss: 0.457 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 480 \tTrain Loss: 0.259 \tTrain Accuracy: 88.00\n",
      "Epoch: 1/10 \tIteration: 490 \tTrain Loss: 0.373 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 500 \tTrain Loss: 0.336 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 510 \tTrain Loss: 0.495 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 520 \tTrain Loss: 0.320 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 530 \tTrain Loss: 0.376 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 540 \tTrain Loss: 0.274 \tTrain Accuracy: 90.00\n",
      "Epoch: 1/10 \tIteration: 550 \tTrain Loss: 0.473 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 560 \tTrain Loss: 0.428 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 570 \tTrain Loss: 0.390 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 580 \tTrain Loss: 0.492 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 590 \tTrain Loss: 0.448 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 600 \tTrain Loss: 0.306 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 610 \tTrain Loss: 0.453 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 620 \tTrain Loss: 0.383 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 630 \tTrain Loss: 0.396 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 640 \tTrain Loss: 0.468 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 650 \tTrain Loss: 0.553 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 660 \tTrain Loss: 0.480 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 670 \tTrain Loss: 0.289 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 680 \tTrain Loss: 0.482 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 690 \tTrain Loss: 0.341 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 700 \tTrain Loss: 0.397 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 710 \tTrain Loss: 0.543 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 720 \tTrain Loss: 0.343 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 730 \tTrain Loss: 0.414 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 740 \tTrain Loss: 0.417 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 750 \tTrain Loss: 0.391 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 760 \tTrain Loss: 0.339 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 770 \tTrain Loss: 0.457 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 780 \tTrain Loss: 0.727 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 790 \tTrain Loss: 0.452 \tTrain Accuracy: 78.00\n",
      "Val Loss: 0.540 \tVal Acc: 76.560\n",
      "Time to train epoch: 29.33099913597107 s\n",
      "Epoch: 2/10 \tIteration: 800 \tTrain Loss: 0.293 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 810 \tTrain Loss: 0.409 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 820 \tTrain Loss: 0.327 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 830 \tTrain Loss: 0.492 \tTrain Accuracy: 74.00\n",
      "Epoch: 2/10 \tIteration: 840 \tTrain Loss: 0.184 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 850 \tTrain Loss: 0.285 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 860 \tTrain Loss: 0.209 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 870 \tTrain Loss: 0.282 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 880 \tTrain Loss: 0.263 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 890 \tTrain Loss: 0.330 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 900 \tTrain Loss: 0.104 \tTrain Accuracy: 100.00\n",
      "Epoch: 2/10 \tIteration: 910 \tTrain Loss: 0.261 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 920 \tTrain Loss: 0.203 \tTrain Accuracy: 94.00\n",
      "Epoch: 2/10 \tIteration: 930 \tTrain Loss: 0.326 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 940 \tTrain Loss: 0.323 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 950 \tTrain Loss: 0.304 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 960 \tTrain Loss: 0.248 \tTrain Accuracy: 94.00\n",
      "Epoch: 2/10 \tIteration: 970 \tTrain Loss: 0.313 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 980 \tTrain Loss: 0.369 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 990 \tTrain Loss: 0.266 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1000 \tTrain Loss: 0.284 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1010 \tTrain Loss: 0.354 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1020 \tTrain Loss: 0.193 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 1030 \tTrain Loss: 0.225 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1040 \tTrain Loss: 0.240 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 1050 \tTrain Loss: 0.338 \tTrain Accuracy: 78.00\n",
      "Epoch: 2/10 \tIteration: 1060 \tTrain Loss: 0.379 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1070 \tTrain Loss: 0.485 \tTrain Accuracy: 80.00\n",
      "Epoch: 2/10 \tIteration: 1080 \tTrain Loss: 0.576 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1090 \tTrain Loss: 0.328 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1100 \tTrain Loss: 0.107 \tTrain Accuracy: 100.00\n",
      "Epoch: 2/10 \tIteration: 1110 \tTrain Loss: 0.340 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1120 \tTrain Loss: 0.263 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 1130 \tTrain Loss: 0.358 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1140 \tTrain Loss: 0.175 \tTrain Accuracy: 94.00\n",
      "Epoch: 2/10 \tIteration: 1150 \tTrain Loss: 0.438 \tTrain Accuracy: 76.00\n",
      "Epoch: 2/10 \tIteration: 1160 \tTrain Loss: 0.264 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1170 \tTrain Loss: 0.519 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 1180 \tTrain Loss: 0.200 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1190 \tTrain Loss: 0.308 \tTrain Accuracy: 84.00\n",
      "Val Loss: 0.552 \tVal Acc: 78.920\n",
      "Time to train epoch: 29.39862060546875 s\n",
      "Epoch: 3/10 \tIteration: 1200 \tTrain Loss: 0.185 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1210 \tTrain Loss: 0.158 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1220 \tTrain Loss: 0.186 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1230 \tTrain Loss: 0.227 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1240 \tTrain Loss: 0.058 \tTrain Accuracy: 100.00\n",
      "Epoch: 3/10 \tIteration: 1250 \tTrain Loss: 0.135 \tTrain Accuracy: 98.00\n",
      "Epoch: 3/10 \tIteration: 1260 \tTrain Loss: 0.118 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1270 \tTrain Loss: 0.149 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1280 \tTrain Loss: 0.199 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1290 \tTrain Loss: 0.157 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1300 \tTrain Loss: 0.179 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1310 \tTrain Loss: 0.129 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1320 \tTrain Loss: 0.315 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1330 \tTrain Loss: 0.168 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1340 \tTrain Loss: 0.173 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1350 \tTrain Loss: 0.417 \tTrain Accuracy: 86.00\n",
      "Epoch: 3/10 \tIteration: 1360 \tTrain Loss: 0.229 \tTrain Accuracy: 86.00\n",
      "Epoch: 3/10 \tIteration: 1370 \tTrain Loss: 0.120 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1380 \tTrain Loss: 0.212 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1390 \tTrain Loss: 0.196 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1400 \tTrain Loss: 0.173 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1410 \tTrain Loss: 0.118 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1420 \tTrain Loss: 0.179 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1430 \tTrain Loss: 0.333 \tTrain Accuracy: 82.00\n",
      "Epoch: 3/10 \tIteration: 1440 \tTrain Loss: 0.124 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1450 \tTrain Loss: 0.100 \tTrain Accuracy: 98.00\n",
      "Epoch: 3/10 \tIteration: 1460 \tTrain Loss: 0.103 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1470 \tTrain Loss: 0.097 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1480 \tTrain Loss: 0.315 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1490 \tTrain Loss: 0.165 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1500 \tTrain Loss: 0.150 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1510 \tTrain Loss: 0.118 \tTrain Accuracy: 98.00\n",
      "Epoch: 3/10 \tIteration: 1520 \tTrain Loss: 0.155 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1530 \tTrain Loss: 0.147 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1540 \tTrain Loss: 0.185 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1550 \tTrain Loss: 0.104 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1560 \tTrain Loss: 0.220 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1570 \tTrain Loss: 0.169 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1580 \tTrain Loss: 0.259 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1590 \tTrain Loss: 0.187 \tTrain Accuracy: 96.00\n",
      "Val Loss: 0.592 \tVal Acc: 80.280\n",
      "Time to train epoch: 29.45642614364624 s\n",
      "Epoch: 4/10 \tIteration: 1600 \tTrain Loss: 0.078 \tTrain Accuracy: 98.00\n",
      "Epoch: 4/10 \tIteration: 1610 \tTrain Loss: 0.044 \tTrain Accuracy: 100.00\n",
      "Epoch: 4/10 \tIteration: 1620 \tTrain Loss: 0.113 \tTrain Accuracy: 96.00\n",
      "Epoch: 4/10 \tIteration: 1630 \tTrain Loss: 0.082 \tTrain Accuracy: 96.00\n",
      "Epoch: 4/10 \tIteration: 1640 \tTrain Loss: 0.150 \tTrain Accuracy: 98.00\n",
      "Epoch: 4/10 \tIteration: 1650 \tTrain Loss: 0.266 \tTrain Accuracy: 90.00\n",
      "Epoch: 4/10 \tIteration: 1660 \tTrain Loss: 0.206 \tTrain Accuracy: 90.00\n",
      "Epoch: 4/10 \tIteration: 1670 \tTrain Loss: 0.193 \tTrain Accuracy: 90.00\n",
      "Epoch: 4/10 \tIteration: 1680 \tTrain Loss: 0.197 \tTrain Accuracy: 90.00\n",
      "Epoch: 4/10 \tIteration: 1690 \tTrain Loss: 0.115 \tTrain Accuracy: 94.00\n",
      "Epoch: 4/10 \tIteration: 1700 \tTrain Loss: 0.079 \tTrain Accuracy: 96.00\n",
      "Epoch: 4/10 \tIteration: 1710 \tTrain Loss: 0.238 \tTrain Accuracy: 86.00\n",
      "Epoch: 4/10 \tIteration: 1720 \tTrain Loss: 0.146 \tTrain Accuracy: 92.00\n",
      "Epoch: 4/10 \tIteration: 1730 \tTrain Loss: 0.078 \tTrain Accuracy: 98.00\n",
      "Epoch: 4/10 \tIteration: 1740 \tTrain Loss: 0.107 \tTrain Accuracy: 96.00\n",
      "Epoch: 4/10 \tIteration: 1750 \tTrain Loss: 0.054 \tTrain Accuracy: 98.00\n",
      "Epoch: 4/10 \tIteration: 1760 \tTrain Loss: 0.112 \tTrain Accuracy: 92.00\n",
      "Epoch: 4/10 \tIteration: 1770 \tTrain Loss: 0.119 \tTrain Accuracy: 96.00\n",
      "Epoch: 4/10 \tIteration: 1780 \tTrain Loss: 0.061 \tTrain Accuracy: 96.00\n",
      "Epoch: 4/10 \tIteration: 1790 \tTrain Loss: 0.082 \tTrain Accuracy: 98.00\n",
      "Epoch: 4/10 \tIteration: 1800 \tTrain Loss: 0.178 \tTrain Accuracy: 92.00\n",
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      "Epoch: 9/10 \tIteration: 3930 \tTrain Loss: 0.035 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3940 \tTrain Loss: 0.083 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3950 \tTrain Loss: 0.140 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3960 \tTrain Loss: 0.209 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3970 \tTrain Loss: 0.061 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3980 \tTrain Loss: 0.051 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3990 \tTrain Loss: 0.078 \tTrain Accuracy: 98.00\n",
      "Val Loss: 0.858 \tVal Acc: 81.600\n",
      "Time to train epoch: 29.41541290283203 s\n",
      "Test Loss: 0.911 \tTest Acc: 81.120\n"
     ]
    }
   ],
   "source": [
    "class BiLSTM(nn.Module):\n",
    "    def __init__(self,vocab_size,embed_size,num_units,output_size,dropout,seq_len,batch_size):\n",
    "        super(BiLSTM, self).__init__()\n",
    "        self.embedding = torch.nn.Embedding(vocab_size,embed_size)\n",
    "        self.batch_size = batch_size\n",
    "        self.rnn = torch.nn.LSTM(embed_size,num_units,num_layers=1,bidirectional=True)\n",
    "        self.dense = torch.nn.Linear(seq_len*num_units*2,output_size)\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "    def forward(self,x,target):\n",
    "        embed_input = self.embedding(x)\n",
    "        embed_input = self.dropout(embed_input)\n",
    "        output, hidden = self.rnn(embed_input)\n",
    "\n",
    "        logits = self.dense(output.view([self.batch_size,-1]))\n",
    "        prediction = torch.sigmoid(logits)\n",
    "        target = target.view([-1,1])\n",
    "        correct_pred = torch.eq(torch.round(prediction).type(target.type()),target)\n",
    "        accuracy = torch.sum(correct_pred)\n",
    "        return prediction, accuracy\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "epochs = 10\n",
    "batch_size = 50\n",
    "num_units = 256\n",
    "learning_rate = 0.001\n",
    "embed_dims = 300\n",
    "seq_len = 200\n",
    "dropout = 0.5\n",
    "vocab_size = len(vocab_to_int)+1\n",
    "\n",
    "model = BiLSTM(vocab_size,embed_dims,num_units,1,dropout,seq_len,batch_size).to(device)\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
    "counter = 0\n",
    "BiLSTM_acc = []\n",
    "BiLSTM_valacc = []\n",
    "model.train()\n",
    "for e in range(epochs):\n",
    "  start_time = time.time()\n",
    "  for inputs, labels in train_loader:\n",
    "    \n",
    "    inputs, labels = inputs.cuda(), labels.cuda()\n",
    "    model.zero_grad()\n",
    "    logits, accuracy = model(inputs,labels)\n",
    "    loss = criterion(logits,labels.float())\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "    optimizer.step()\n",
    "    if counter%10==0:\n",
    "        print(\"Epoch: {}/{}\".format(e,epochs),\n",
    "                     \"\\tIteration: {}\".format(counter),\n",
    "                     \"\\tTrain Loss: {:.3f}\".format(loss.item()),\n",
    "                     \"\\tTrain Accuracy: {:.2f}\".format(accuracy.item()*100/batch_size))\n",
    "        BiLSTM_acc.append(accuracy.item()*100/batch_size)\n",
    "    if counter%399==0:\n",
    "            with torch.no_grad():\n",
    "                model.eval()\n",
    "                val_acc = []\n",
    "                val_loss = []\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs_val, labels_val = inputs.cuda(), labels.cuda()\n",
    "                    logits_val,accuracy_val = model(inputs_val,labels_val)\n",
    "                    loss_val = criterion(logits_val,labels_val.float())\n",
    "                    val_acc.append(accuracy_val.item()*100/batch_size)\n",
    "                    val_loss.append(loss_val.item())\n",
    "                print(\"Val Loss: {:.3f}\".format(np.mean(val_loss)), \"\\tVal Acc: {:.3f}\".format(np.mean(val_acc)))\n",
    "                BiLSTM_valacc.append(np.mean(val_acc))\n",
    "                model.train()\n",
    "    counter += 1\n",
    "  print(\"Time to train epoch: {0} s\".format(time.time()-start_time))  \n",
    "with torch.no_grad():\n",
    "  model.eval()\n",
    "  test_acc = []\n",
    "  test_loss = []\n",
    "  for inputs, labels in test_loader:\n",
    "    input_test, labels_test = inputs.cuda(), labels.cuda()\n",
    "    logits_test,accuracy_test = model(input_test,labels_test)\n",
    "    loss_test = criterion(logits_test,labels_test.float())\n",
    "    test_acc.append(accuracy_test.item()*100/batch_size)\n",
    "    test_loss.append(loss_test.item())\n",
    "print(\"Test Loss: {:.3f}\".format(np.mean(test_loss)), \"\\tTest Acc: {:.3f}\".format(np.mean(test_acc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "quqQs7XJjPa5"
   },
   "source": [
    "## Multi-Layer Bidirectional LSTM\n",
    "A 2 layer bidirectional LSTM with 256 in each direction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7245
    },
    "colab_type": "code",
    "id": "uo_3_eAZjPRA",
    "outputId": "3ad1d7af-77ca-426c-ac61-a20043be42d6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 \tIteration: 0 \tTrain Loss: 0.695 \tTrain Accuracy: 52.00\n",
      "Val Loss: 0.947 \tVal Acc: 50.000\n",
      "Epoch: 0/10 \tIteration: 10 \tTrain Loss: 0.688 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 20 \tTrain Loss: 0.703 \tTrain Accuracy: 46.00\n",
      "Epoch: 0/10 \tIteration: 30 \tTrain Loss: 0.700 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 40 \tTrain Loss: 0.723 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 50 \tTrain Loss: 0.705 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 60 \tTrain Loss: 0.710 \tTrain Accuracy: 44.00\n",
      "Epoch: 0/10 \tIteration: 70 \tTrain Loss: 0.695 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 80 \tTrain Loss: 0.700 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 90 \tTrain Loss: 0.659 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 100 \tTrain Loss: 0.695 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 110 \tTrain Loss: 0.845 \tTrain Accuracy: 44.00\n",
      "Epoch: 0/10 \tIteration: 120 \tTrain Loss: 0.675 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 130 \tTrain Loss: 0.709 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 140 \tTrain Loss: 0.733 \tTrain Accuracy: 50.00\n",
      "Epoch: 0/10 \tIteration: 150 \tTrain Loss: 0.677 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 160 \tTrain Loss: 0.696 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 170 \tTrain Loss: 0.671 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 180 \tTrain Loss: 0.608 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 190 \tTrain Loss: 0.668 \tTrain Accuracy: 70.00\n",
      "Epoch: 0/10 \tIteration: 200 \tTrain Loss: 0.709 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 210 \tTrain Loss: 0.713 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 220 \tTrain Loss: 0.714 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 230 \tTrain Loss: 0.597 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 240 \tTrain Loss: 0.540 \tTrain Accuracy: 76.00\n",
      "Epoch: 0/10 \tIteration: 250 \tTrain Loss: 0.584 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 260 \tTrain Loss: 0.686 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 270 \tTrain Loss: 0.442 \tTrain Accuracy: 80.00\n",
      "Epoch: 0/10 \tIteration: 280 \tTrain Loss: 0.658 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 290 \tTrain Loss: 0.632 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 300 \tTrain Loss: 0.723 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 310 \tTrain Loss: 0.583 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 320 \tTrain Loss: 0.551 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 330 \tTrain Loss: 0.530 \tTrain Accuracy: 68.00\n",
      "Epoch: 0/10 \tIteration: 340 \tTrain Loss: 0.638 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 350 \tTrain Loss: 0.540 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 360 \tTrain Loss: 0.567 \tTrain Accuracy: 74.00\n",
      "Epoch: 0/10 \tIteration: 370 \tTrain Loss: 0.391 \tTrain Accuracy: 86.00\n",
      "Epoch: 0/10 \tIteration: 380 \tTrain Loss: 0.626 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 390 \tTrain Loss: 0.894 \tTrain Accuracy: 64.00\n",
      "Val Loss: 0.546 \tVal Acc: 73.080\n",
      "Time to train epoch: 56.925525426864624 s\n",
      "Epoch: 1/10 \tIteration: 400 \tTrain Loss: 0.456 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 410 \tTrain Loss: 0.506 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 420 \tTrain Loss: 0.287 \tTrain Accuracy: 92.00\n",
      "Epoch: 1/10 \tIteration: 430 \tTrain Loss: 0.561 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 440 \tTrain Loss: 0.467 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 450 \tTrain Loss: 0.766 \tTrain Accuracy: 64.00\n",
      "Epoch: 1/10 \tIteration: 460 \tTrain Loss: 0.555 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 470 \tTrain Loss: 0.323 \tTrain Accuracy: 92.00\n",
      "Epoch: 1/10 \tIteration: 480 \tTrain Loss: 0.431 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 490 \tTrain Loss: 0.535 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 500 \tTrain Loss: 0.378 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 510 \tTrain Loss: 0.376 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 520 \tTrain Loss: 0.340 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 530 \tTrain Loss: 0.541 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 540 \tTrain Loss: 0.461 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 550 \tTrain Loss: 0.400 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 560 \tTrain Loss: 0.402 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 570 \tTrain Loss: 0.527 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 580 \tTrain Loss: 0.358 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 590 \tTrain Loss: 0.592 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 600 \tTrain Loss: 0.431 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 610 \tTrain Loss: 0.481 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 620 \tTrain Loss: 0.489 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 630 \tTrain Loss: 0.300 \tTrain Accuracy: 90.00\n",
      "Epoch: 1/10 \tIteration: 640 \tTrain Loss: 0.523 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 650 \tTrain Loss: 0.406 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 660 \tTrain Loss: 0.544 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 670 \tTrain Loss: 0.303 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 680 \tTrain Loss: 0.430 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 690 \tTrain Loss: 0.548 \tTrain Accuracy: 66.00\n",
      "Epoch: 1/10 \tIteration: 700 \tTrain Loss: 0.509 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 710 \tTrain Loss: 0.519 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 720 \tTrain Loss: 0.362 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 730 \tTrain Loss: 0.417 \tTrain Accuracy: 86.00\n",
      "Epoch: 1/10 \tIteration: 740 \tTrain Loss: 0.276 \tTrain Accuracy: 90.00\n",
      "Epoch: 1/10 \tIteration: 750 \tTrain Loss: 0.279 \tTrain Accuracy: 92.00\n",
      "Epoch: 1/10 \tIteration: 760 \tTrain Loss: 0.365 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 770 \tTrain Loss: 0.362 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 780 \tTrain Loss: 0.247 \tTrain Accuracy: 92.00\n",
      "Epoch: 1/10 \tIteration: 790 \tTrain Loss: 0.389 \tTrain Accuracy: 86.00\n",
      "Val Loss: 0.448 \tVal Acc: 80.840\n",
      "Time to train epoch: 54.977575063705444 s\n",
      "Epoch: 2/10 \tIteration: 800 \tTrain Loss: 0.257 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 810 \tTrain Loss: 0.294 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 820 \tTrain Loss: 0.265 \tTrain Accuracy: 94.00\n",
      "Epoch: 2/10 \tIteration: 830 \tTrain Loss: 0.280 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 840 \tTrain Loss: 0.329 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 850 \tTrain Loss: 0.608 \tTrain Accuracy: 74.00\n",
      "Epoch: 2/10 \tIteration: 860 \tTrain Loss: 0.262 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 870 \tTrain Loss: 0.310 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 880 \tTrain Loss: 0.452 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 890 \tTrain Loss: 0.266 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 900 \tTrain Loss: 0.365 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 910 \tTrain Loss: 0.350 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 920 \tTrain Loss: 0.383 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 930 \tTrain Loss: 0.222 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 940 \tTrain Loss: 0.325 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 950 \tTrain Loss: 0.372 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 960 \tTrain Loss: 0.256 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 970 \tTrain Loss: 0.253 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 980 \tTrain Loss: 0.408 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 990 \tTrain Loss: 0.233 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1000 \tTrain Loss: 0.326 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 1010 \tTrain Loss: 0.357 \tTrain Accuracy: 80.00\n",
      "Epoch: 2/10 \tIteration: 1020 \tTrain Loss: 0.479 \tTrain Accuracy: 72.00\n",
      "Epoch: 2/10 \tIteration: 1030 \tTrain Loss: 0.265 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1040 \tTrain Loss: 0.313 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1050 \tTrain Loss: 0.344 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1060 \tTrain Loss: 0.264 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 1070 \tTrain Loss: 0.282 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 1080 \tTrain Loss: 0.355 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1090 \tTrain Loss: 0.224 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1100 \tTrain Loss: 0.213 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1110 \tTrain Loss: 0.252 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 1120 \tTrain Loss: 0.247 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1130 \tTrain Loss: 0.293 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1140 \tTrain Loss: 0.386 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1150 \tTrain Loss: 0.345 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 1160 \tTrain Loss: 0.330 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1170 \tTrain Loss: 0.280 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 1180 \tTrain Loss: 0.370 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 1190 \tTrain Loss: 0.395 \tTrain Accuracy: 82.00\n",
      "Val Loss: 0.476 \tVal Acc: 80.960\n",
      "Time to train epoch: 55.20221519470215 s\n",
      "Epoch: 3/10 \tIteration: 1200 \tTrain Loss: 0.157 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1210 \tTrain Loss: 0.247 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1220 \tTrain Loss: 0.309 \tTrain Accuracy: 84.00\n",
      "Epoch: 3/10 \tIteration: 1230 \tTrain Loss: 0.359 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1240 \tTrain Loss: 0.205 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1250 \tTrain Loss: 0.276 \tTrain Accuracy: 86.00\n",
      "Epoch: 3/10 \tIteration: 1260 \tTrain Loss: 0.170 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1270 \tTrain Loss: 0.172 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1280 \tTrain Loss: 0.215 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1290 \tTrain Loss: 0.366 \tTrain Accuracy: 80.00\n",
      "Epoch: 3/10 \tIteration: 1300 \tTrain Loss: 0.308 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1310 \tTrain Loss: 0.266 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1320 \tTrain Loss: 0.131 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1330 \tTrain Loss: 0.240 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1340 \tTrain Loss: 0.344 \tTrain Accuracy: 80.00\n",
      "Epoch: 3/10 \tIteration: 1350 \tTrain Loss: 0.291 \tTrain Accuracy: 86.00\n",
      "Epoch: 3/10 \tIteration: 1360 \tTrain Loss: 0.215 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1370 \tTrain Loss: 0.189 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1380 \tTrain Loss: 0.357 \tTrain Accuracy: 84.00\n",
      "Epoch: 3/10 \tIteration: 1390 \tTrain Loss: 0.201 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1400 \tTrain Loss: 0.259 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1410 \tTrain Loss: 0.309 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1420 \tTrain Loss: 0.284 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1430 \tTrain Loss: 0.261 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1440 \tTrain Loss: 0.215 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1450 \tTrain Loss: 0.208 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1460 \tTrain Loss: 0.312 \tTrain Accuracy: 90.00\n",
      "Epoch: 3/10 \tIteration: 1470 \tTrain Loss: 0.197 \tTrain Accuracy: 96.00\n",
      "Epoch: 3/10 \tIteration: 1480 \tTrain Loss: 0.306 \tTrain Accuracy: 88.00\n",
      "Epoch: 3/10 \tIteration: 1490 \tTrain Loss: 0.223 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1500 \tTrain Loss: 0.209 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1510 \tTrain Loss: 0.244 \tTrain Accuracy: 92.00\n",
      "Epoch: 3/10 \tIteration: 1520 \tTrain Loss: 0.145 \tTrain Accuracy: 94.00\n",
      "Epoch: 3/10 \tIteration: 1530 \tTrain Loss: 0.308 \tTrain Accuracy: 86.00\n",
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      "Epoch: 9/10 \tIteration: 3650 \tTrain Loss: 0.054 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3660 \tTrain Loss: 0.081 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3670 \tTrain Loss: 0.050 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3680 \tTrain Loss: 0.153 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3690 \tTrain Loss: 0.038 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3700 \tTrain Loss: 0.030 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3710 \tTrain Loss: 0.046 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3720 \tTrain Loss: 0.084 \tTrain Accuracy: 94.00\n",
      "Epoch: 9/10 \tIteration: 3730 \tTrain Loss: 0.083 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3740 \tTrain Loss: 0.070 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3750 \tTrain Loss: 0.086 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3760 \tTrain Loss: 0.117 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3770 \tTrain Loss: 0.040 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3780 \tTrain Loss: 0.054 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3790 \tTrain Loss: 0.026 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3800 \tTrain Loss: 0.035 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3810 \tTrain Loss: 0.054 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3820 \tTrain Loss: 0.004 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3830 \tTrain Loss: 0.096 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3840 \tTrain Loss: 0.063 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3850 \tTrain Loss: 0.074 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3860 \tTrain Loss: 0.014 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3870 \tTrain Loss: 0.077 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3880 \tTrain Loss: 0.013 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3890 \tTrain Loss: 0.027 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3900 \tTrain Loss: 0.073 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3910 \tTrain Loss: 0.057 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3920 \tTrain Loss: 0.017 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3930 \tTrain Loss: 0.054 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3940 \tTrain Loss: 0.066 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3950 \tTrain Loss: 0.023 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3960 \tTrain Loss: 0.037 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3970 \tTrain Loss: 0.041 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3980 \tTrain Loss: 0.063 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3990 \tTrain Loss: 0.050 \tTrain Accuracy: 96.00\n",
      "Val Loss: 0.902 \tVal Acc: 80.440\n",
      "Time to train epoch: 57.35245084762573 s\n",
      "Test Loss: 0.914 \tTest Acc: 81.040\n"
     ]
    }
   ],
   "source": [
    "class MBiLSTM(nn.Module):\n",
    "    def __init__(self,vocab_size,embed_size,num_units,output_size,dropout,seq_len,batch_size,num_layers):\n",
    "        super(MBiLSTM, self).__init__()\n",
    "        self.embedding = torch.nn.Embedding(vocab_size,embed_size)\n",
    "        self.batch_size = batch_size\n",
    "        self.rnn = torch.nn.LSTM(embed_size,num_units,num_layers=num_layers,bidirectional=True)\n",
    "        self.dense = torch.nn.Linear(seq_len*num_units*2,output_size)\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "    def forward(self,x,target):\n",
    "        embed_input = self.embedding(x)\n",
    "        embed_input = self.dropout(embed_input)\n",
    "        output, hidden = self.rnn(embed_input)\n",
    "        \n",
    "        logits = self.dense(output.view([self.batch_size,-1]))\n",
    "        prediction = torch.sigmoid(logits)\n",
    "        target = target.view([-1,1])\n",
    "        correct_pred = torch.eq(torch.round(prediction).type(target.type()),target)\n",
    "        accuracy = torch.sum(correct_pred)\n",
    "        return prediction, accuracy\n",
    "      \n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "epochs = 10\n",
    "batch_size = 50\n",
    "num_units = 256\n",
    "learning_rate = 0.001\n",
    "embed_dims = 300\n",
    "seq_len = 200\n",
    "dropout = 0.5\n",
    "vocab_size = len(vocab_to_int)+1\n",
    "num_layers = 2\n",
    "\n",
    "model = MBiLSTM(vocab_size,embed_dims,num_units,1,dropout,seq_len,batch_size,num_layers).to(device)\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
    "counter = 0\n",
    "MBiLSTM_acc = []\n",
    "MBiLSTM_valacc = []\n",
    "model.train()\n",
    "for e in range(epochs):\n",
    "  start_time = time.time()\n",
    "  for inputs, labels in train_loader:\n",
    "    \n",
    "    inputs, labels = inputs.cuda(), labels.cuda()\n",
    "    model.zero_grad()\n",
    "    logits, accuracy = model(inputs,labels)\n",
    "    loss = criterion(logits,labels.float())\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "    optimizer.step()\n",
    "    if counter%10==0:\n",
    "        print(\"Epoch: {}/{}\".format(e,epochs),\n",
    "                     \"\\tIteration: {}\".format(counter),\n",
    "                     \"\\tTrain Loss: {:.3f}\".format(loss.item()),\n",
    "                     \"\\tTrain Accuracy: {:.2f}\".format(accuracy.item()*100/batch_size))\n",
    "        MBiLSTM_acc.append(accuracy.item()*100/batch_size)\n",
    "    if counter%399==0:\n",
    "            with torch.no_grad():\n",
    "                model.eval()\n",
    "                val_acc = []\n",
    "                val_loss = []\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs_val, labels_val = inputs.cuda(), labels.cuda()\n",
    "                    logits_val,accuracy_val = model(inputs_val,labels_val)\n",
    "                    loss_val = criterion(logits_val,labels_val.float())\n",
    "                    val_acc.append(accuracy_val.item()*100/batch_size)\n",
    "                    val_loss.append(loss_val.item())\n",
    "                print(\"Val Loss: {:.3f}\".format(np.mean(val_loss)), \"\\tVal Acc: {:.3f}\".format(np.mean(val_acc)))\n",
    "                MBiLSTM_valacc.append(np.mean(val_acc))\n",
    "                model.train()\n",
    "    counter += 1\n",
    "  print(\"Time to train epoch: {0} s\".format(time.time()-start_time))  \n",
    "with torch.no_grad():\n",
    "  model.eval()\n",
    "  test_acc = []\n",
    "  test_loss = []\n",
    "  for inputs, labels in test_loader:\n",
    "    input_test, labels_test = inputs.cuda(), labels.cuda()\n",
    "    logits_test,accuracy_test = model(input_test,labels_test)\n",
    "    loss_test = criterion(logits_test,labels_test.float())\n",
    "    test_acc.append(accuracy_test.item()*100/batch_size)\n",
    "    test_loss.append(loss_test.item())\n",
    "print(\"Test Loss: {:.3f}\".format(np.mean(test_loss)), \"\\tTest Acc: {:.3f}\".format(np.mean(test_acc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NZ3tL8CVjm_u"
   },
   "source": [
    "## Convolutional Neural Network\n",
    "A CNN sentiment analysis architecture based on the implementation in https://arxiv.org/abs/1408.5882. Three kernels are used of size 3,4,5 with 100 filters for each kernel. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7245
    },
    "colab_type": "code",
    "id": "zSPCnys-jtmG",
    "outputId": "5ffe4a8d-2d58-4f3a-bd86-a5cacaf04aa1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2016: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/10 \tIteration: 0 \t\tTrain Loss: 0.786 \tTrain Accuracy: 52.00\n",
      "\t\tVal Loss: 0.732 \t\tVal Acc: 50.000\n",
      "Epoch: 0/10 \tIteration: 10 \t\tTrain Loss: 0.700 \tTrain Accuracy: 40.00\n",
      "Epoch: 0/10 \tIteration: 20 \t\tTrain Loss: 0.704 \tTrain Accuracy: 38.00\n",
      "Epoch: 0/10 \tIteration: 30 \t\tTrain Loss: 0.692 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 40 \t\tTrain Loss: 0.737 \tTrain Accuracy: 36.00\n",
      "Epoch: 0/10 \tIteration: 50 \t\tTrain Loss: 0.678 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 60 \t\tTrain Loss: 0.694 \tTrain Accuracy: 44.00\n",
      "Epoch: 0/10 \tIteration: 70 \t\tTrain Loss: 0.688 \tTrain Accuracy: 54.00\n",
      "Epoch: 0/10 \tIteration: 80 \t\tTrain Loss: 0.677 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 90 \t\tTrain Loss: 0.692 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 100 \t\tTrain Loss: 0.687 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 110 \t\tTrain Loss: 0.688 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 120 \t\tTrain Loss: 0.692 \tTrain Accuracy: 52.00\n",
      "Epoch: 0/10 \tIteration: 130 \t\tTrain Loss: 0.693 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 140 \t\tTrain Loss: 0.680 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 150 \t\tTrain Loss: 0.686 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 160 \t\tTrain Loss: 0.686 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 170 \t\tTrain Loss: 0.704 \tTrain Accuracy: 40.00\n",
      "Epoch: 0/10 \tIteration: 180 \t\tTrain Loss: 0.698 \tTrain Accuracy: 42.00\n",
      "Epoch: 0/10 \tIteration: 190 \t\tTrain Loss: 0.655 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 200 \t\tTrain Loss: 0.703 \tTrain Accuracy: 42.00\n",
      "Epoch: 0/10 \tIteration: 210 \t\tTrain Loss: 0.668 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 220 \t\tTrain Loss: 0.673 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 230 \t\tTrain Loss: 0.666 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 240 \t\tTrain Loss: 0.685 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 250 \t\tTrain Loss: 0.672 \tTrain Accuracy: 62.00\n",
      "Epoch: 0/10 \tIteration: 260 \t\tTrain Loss: 0.687 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 270 \t\tTrain Loss: 0.697 \tTrain Accuracy: 48.00\n",
      "Epoch: 0/10 \tIteration: 280 \t\tTrain Loss: 0.712 \tTrain Accuracy: 44.00\n",
      "Epoch: 0/10 \tIteration: 290 \t\tTrain Loss: 0.670 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 300 \t\tTrain Loss: 0.629 \tTrain Accuracy: 56.00\n",
      "Epoch: 0/10 \tIteration: 310 \t\tTrain Loss: 0.651 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 320 \t\tTrain Loss: 0.642 \tTrain Accuracy: 58.00\n",
      "Epoch: 0/10 \tIteration: 330 \t\tTrain Loss: 0.663 \tTrain Accuracy: 64.00\n",
      "Epoch: 0/10 \tIteration: 340 \t\tTrain Loss: 0.615 \tTrain Accuracy: 66.00\n",
      "Epoch: 0/10 \tIteration: 350 \t\tTrain Loss: 0.621 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 360 \t\tTrain Loss: 0.661 \tTrain Accuracy: 60.00\n",
      "Epoch: 0/10 \tIteration: 370 \t\tTrain Loss: 0.623 \tTrain Accuracy: 70.00\n",
      "Epoch: 0/10 \tIteration: 380 \t\tTrain Loss: 0.606 \tTrain Accuracy: 72.00\n",
      "Epoch: 0/10 \tIteration: 390 \t\tTrain Loss: 0.601 \tTrain Accuracy: 76.00\n",
      "\t\tVal Loss: 0.619 \t\tVal Acc: 64.880\n",
      "Time to train epoch: 20.497713565826416 s\n",
      "Epoch: 1/10 \tIteration: 400 \t\tTrain Loss: 0.615 \tTrain Accuracy: 68.00\n",
      "Epoch: 1/10 \tIteration: 410 \t\tTrain Loss: 0.592 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 420 \t\tTrain Loss: 0.611 \tTrain Accuracy: 62.00\n",
      "Epoch: 1/10 \tIteration: 430 \t\tTrain Loss: 0.567 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 440 \t\tTrain Loss: 0.527 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 450 \t\tTrain Loss: 0.550 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 460 \t\tTrain Loss: 0.493 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 470 \t\tTrain Loss: 0.591 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 480 \t\tTrain Loss: 0.549 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 490 \t\tTrain Loss: 0.628 \tTrain Accuracy: 58.00\n",
      "Epoch: 1/10 \tIteration: 500 \t\tTrain Loss: 0.541 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 510 \t\tTrain Loss: 0.549 \tTrain Accuracy: 66.00\n",
      "Epoch: 1/10 \tIteration: 520 \t\tTrain Loss: 0.490 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 530 \t\tTrain Loss: 0.517 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 540 \t\tTrain Loss: 0.498 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 550 \t\tTrain Loss: 0.403 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 560 \t\tTrain Loss: 0.525 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 570 \t\tTrain Loss: 0.479 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 580 \t\tTrain Loss: 0.490 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 590 \t\tTrain Loss: 0.481 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 600 \t\tTrain Loss: 0.576 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 610 \t\tTrain Loss: 0.477 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 620 \t\tTrain Loss: 0.549 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 630 \t\tTrain Loss: 0.476 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 640 \t\tTrain Loss: 0.465 \tTrain Accuracy: 74.00\n",
      "Epoch: 1/10 \tIteration: 650 \t\tTrain Loss: 0.436 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 660 \t\tTrain Loss: 0.483 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 670 \t\tTrain Loss: 0.507 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 680 \t\tTrain Loss: 0.430 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 690 \t\tTrain Loss: 0.506 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 700 \t\tTrain Loss: 0.477 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 710 \t\tTrain Loss: 0.452 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 720 \t\tTrain Loss: 0.438 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 730 \t\tTrain Loss: 0.446 \tTrain Accuracy: 78.00\n",
      "Epoch: 1/10 \tIteration: 740 \t\tTrain Loss: 0.426 \tTrain Accuracy: 82.00\n",
      "Epoch: 1/10 \tIteration: 750 \t\tTrain Loss: 0.441 \tTrain Accuracy: 76.00\n",
      "Epoch: 1/10 \tIteration: 760 \t\tTrain Loss: 0.504 \tTrain Accuracy: 72.00\n",
      "Epoch: 1/10 \tIteration: 770 \t\tTrain Loss: 0.454 \tTrain Accuracy: 80.00\n",
      "Epoch: 1/10 \tIteration: 780 \t\tTrain Loss: 0.423 \tTrain Accuracy: 84.00\n",
      "Epoch: 1/10 \tIteration: 790 \t\tTrain Loss: 0.526 \tTrain Accuracy: 74.00\n",
      "\t\tVal Loss: 0.500 \t\tVal Acc: 75.040\n",
      "Time to train epoch: 19.691826581954956 s\n",
      "Epoch: 2/10 \tIteration: 800 \t\tTrain Loss: 0.385 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 810 \t\tTrain Loss: 0.535 \tTrain Accuracy: 78.00\n",
      "Epoch: 2/10 \tIteration: 820 \t\tTrain Loss: 0.416 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 830 \t\tTrain Loss: 0.252 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 840 \t\tTrain Loss: 0.245 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 850 \t\tTrain Loss: 0.261 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 860 \t\tTrain Loss: 0.339 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 870 \t\tTrain Loss: 0.353 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 880 \t\tTrain Loss: 0.395 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 890 \t\tTrain Loss: 0.427 \tTrain Accuracy: 74.00\n",
      "Epoch: 2/10 \tIteration: 900 \t\tTrain Loss: 0.322 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 910 \t\tTrain Loss: 0.255 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 920 \t\tTrain Loss: 0.456 \tTrain Accuracy: 76.00\n",
      "Epoch: 2/10 \tIteration: 930 \t\tTrain Loss: 0.276 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 940 \t\tTrain Loss: 0.511 \tTrain Accuracy: 74.00\n",
      "Epoch: 2/10 \tIteration: 950 \t\tTrain Loss: 0.345 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 960 \t\tTrain Loss: 0.289 \tTrain Accuracy: 90.00\n",
      "Epoch: 2/10 \tIteration: 970 \t\tTrain Loss: 0.392 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 980 \t\tTrain Loss: 0.348 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 990 \t\tTrain Loss: 0.291 \tTrain Accuracy: 86.00\n",
      "Epoch: 2/10 \tIteration: 1000 \t\tTrain Loss: 0.227 \tTrain Accuracy: 94.00\n",
      "Epoch: 2/10 \tIteration: 1010 \t\tTrain Loss: 0.177 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1020 \t\tTrain Loss: 0.252 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1030 \t\tTrain Loss: 0.294 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1040 \t\tTrain Loss: 0.354 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 1050 \t\tTrain Loss: 0.356 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1060 \t\tTrain Loss: 0.399 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 1070 \t\tTrain Loss: 0.411 \tTrain Accuracy: 76.00\n",
      "Epoch: 2/10 \tIteration: 1080 \t\tTrain Loss: 0.435 \tTrain Accuracy: 74.00\n",
      "Epoch: 2/10 \tIteration: 1090 \t\tTrain Loss: 0.327 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1100 \t\tTrain Loss: 0.289 \tTrain Accuracy: 84.00\n",
      "Epoch: 2/10 \tIteration: 1110 \t\tTrain Loss: 0.372 \tTrain Accuracy: 78.00\n",
      "Epoch: 2/10 \tIteration: 1120 \t\tTrain Loss: 0.256 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1130 \t\tTrain Loss: 0.318 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1140 \t\tTrain Loss: 0.265 \tTrain Accuracy: 92.00\n",
      "Epoch: 2/10 \tIteration: 1150 \t\tTrain Loss: 0.323 \tTrain Accuracy: 88.00\n",
      "Epoch: 2/10 \tIteration: 1160 \t\tTrain Loss: 0.363 \tTrain Accuracy: 82.00\n",
      "Epoch: 2/10 \tIteration: 1170 \t\tTrain Loss: 0.302 \tTrain Accuracy: 88.00\n",
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      "\t\tVal Loss: 0.497 \t\tVal Acc: 75.160\n",
      "Time to train epoch: 19.692214727401733 s\n",
      "Epoch: 3/10 \tIteration: 1200 \t\tTrain Loss: 0.245 \tTrain Accuracy: 90.00\n",
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      "\t\tVal Loss: 0.521 \t\tVal Acc: 76.480\n",
      "Time to train epoch: 19.71250295639038 s\n",
      "Epoch: 4/10 \tIteration: 1600 \t\tTrain Loss: 0.117 \tTrain Accuracy: 98.00\n",
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      "\t\tVal Loss: 0.577 \t\tVal Acc: 76.880\n",
      "Time to train epoch: 19.69107961654663 s\n",
      "Epoch: 5/10 \tIteration: 2000 \t\tTrain Loss: 0.093 \tTrain Accuracy: 98.00\n",
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      "\t\tVal Loss: 0.667 \t\tVal Acc: 76.400\n",
      "Time to train epoch: 19.69311213493347 s\n",
      "Epoch: 6/10 \tIteration: 2400 \t\tTrain Loss: 0.147 \tTrain Accuracy: 96.00\n",
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      "Epoch: 6/10 \tIteration: 2790 \t\tTrain Loss: 0.065 \tTrain Accuracy: 96.00\n",
      "\t\tVal Loss: 0.740 \t\tVal Acc: 76.480\n",
      "Time to train epoch: 19.659793376922607 s\n",
      "Epoch: 7/10 \tIteration: 2800 \t\tTrain Loss: 0.075 \tTrain Accuracy: 96.00\n",
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      "Epoch: 7/10 \tIteration: 3190 \t\tTrain Loss: 0.036 \tTrain Accuracy: 98.00\n",
      "\t\tVal Loss: 0.884 \t\tVal Acc: 75.800\n",
      "Time to train epoch: 19.660674571990967 s\n",
      "Epoch: 8/10 \tIteration: 3200 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3210 \t\tTrain Loss: 0.087 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3220 \t\tTrain Loss: 0.023 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3230 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3240 \t\tTrain Loss: 0.011 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3250 \t\tTrain Loss: 0.035 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3260 \t\tTrain Loss: 0.053 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3270 \t\tTrain Loss: 0.018 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3280 \t\tTrain Loss: 0.028 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3290 \t\tTrain Loss: 0.020 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3300 \t\tTrain Loss: 0.019 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3310 \t\tTrain Loss: 0.040 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3320 \t\tTrain Loss: 0.034 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3330 \t\tTrain Loss: 0.050 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3340 \t\tTrain Loss: 0.087 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3350 \t\tTrain Loss: 0.033 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3360 \t\tTrain Loss: 0.052 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3370 \t\tTrain Loss: 0.025 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3380 \t\tTrain Loss: 0.137 \tTrain Accuracy: 96.00\n",
      "Epoch: 8/10 \tIteration: 3390 \t\tTrain Loss: 0.014 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3400 \t\tTrain Loss: 0.036 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3410 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3420 \t\tTrain Loss: 0.025 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3430 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3440 \t\tTrain Loss: 0.074 \tTrain Accuracy: 96.00\n",
      "Epoch: 8/10 \tIteration: 3450 \t\tTrain Loss: 0.034 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3460 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3470 \t\tTrain Loss: 0.026 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3480 \t\tTrain Loss: 0.027 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3490 \t\tTrain Loss: 0.028 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3500 \t\tTrain Loss: 0.035 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3510 \t\tTrain Loss: 0.021 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3520 \t\tTrain Loss: 0.018 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3530 \t\tTrain Loss: 0.047 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3540 \t\tTrain Loss: 0.043 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3550 \t\tTrain Loss: 0.089 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3560 \t\tTrain Loss: 0.094 \tTrain Accuracy: 98.00\n",
      "Epoch: 8/10 \tIteration: 3570 \t\tTrain Loss: 0.017 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3580 \t\tTrain Loss: 0.033 \tTrain Accuracy: 100.00\n",
      "Epoch: 8/10 \tIteration: 3590 \t\tTrain Loss: 0.036 \tTrain Accuracy: 100.00\n",
      "\t\tVal Loss: 0.955 \t\tVal Acc: 74.640\n",
      "Time to train epoch: 19.634514570236206 s\n",
      "Epoch: 9/10 \tIteration: 3600 \t\tTrain Loss: 0.068 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3610 \t\tTrain Loss: 0.086 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3620 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3630 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3640 \t\tTrain Loss: 0.071 \tTrain Accuracy: 96.00\n",
      "Epoch: 9/10 \tIteration: 3650 \t\tTrain Loss: 0.011 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3660 \t\tTrain Loss: 0.012 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3670 \t\tTrain Loss: 0.017 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3680 \t\tTrain Loss: 0.021 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3690 \t\tTrain Loss: 0.016 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3700 \t\tTrain Loss: 0.011 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3710 \t\tTrain Loss: 0.007 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3720 \t\tTrain Loss: 0.009 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3730 \t\tTrain Loss: 0.006 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3740 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3750 \t\tTrain Loss: 0.028 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3760 \t\tTrain Loss: 0.017 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3770 \t\tTrain Loss: 0.019 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3780 \t\tTrain Loss: 0.072 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3790 \t\tTrain Loss: 0.013 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3800 \t\tTrain Loss: 0.012 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3810 \t\tTrain Loss: 0.019 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3820 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3830 \t\tTrain Loss: 0.016 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3840 \t\tTrain Loss: 0.016 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3850 \t\tTrain Loss: 0.042 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3860 \t\tTrain Loss: 0.018 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3870 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3880 \t\tTrain Loss: 0.022 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3890 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3900 \t\tTrain Loss: 0.011 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3910 \t\tTrain Loss: 0.020 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3920 \t\tTrain Loss: 0.026 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3930 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3940 \t\tTrain Loss: 0.010 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3950 \t\tTrain Loss: 0.015 \tTrain Accuracy: 100.00\n",
      "Epoch: 9/10 \tIteration: 3960 \t\tTrain Loss: 0.025 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3970 \t\tTrain Loss: 0.031 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3980 \t\tTrain Loss: 0.041 \tTrain Accuracy: 98.00\n",
      "Epoch: 9/10 \tIteration: 3990 \t\tTrain Loss: 0.020 \tTrain Accuracy: 100.00\n",
      "\t\tVal Loss: 1.078 \t\tVal Acc: 75.240\n",
      "Time to train epoch: 19.665007829666138 s\n",
      "Test Loss: 1.072 \tTest Acc: 75.560\n"
     ]
    }
   ],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self,vocab_size,embed_size,filter_size,kernel_size,dropout,seq_len):\n",
    "        super(CNN, self).__init__()\n",
    "        self.embedding = torch.nn.Embedding(vocab_size,embed_size)\n",
    "        self.conv1 = torch.nn.Conv2d(1,filter_size,kernel_size=[kernel_size[0],embed_size])\n",
    "        self.conv2 = torch.nn.Conv2d(1,filter_size,kernel_size=[kernel_size[1],embed_size])\n",
    "        self.conv3 = torch.nn.Conv2d(1,filter_size,kernel_size=[kernel_size[2],embed_size])\n",
    "        self.mp1 = torch.nn.MaxPool1d(seq_len+1-kernel_size[0])\n",
    "        self.mp2 = torch.nn.MaxPool1d(seq_len+1-kernel_size[1])\n",
    "        self.mp3 = torch.nn.MaxPool1d(seq_len+1-kernel_size[2])\n",
    "        self.dense = torch.nn.Linear(filter_size*3,1)\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "    def forward(self,x,target):\n",
    "        embed_input = self.embedding(x)\n",
    "        embed_input.unsqueeze_(1)\n",
    "        x1 = torch.tanh(self.dropout(self.conv1(embed_input))).squeeze(3)\n",
    "        x2 = torch.tanh(self.dropout(self.conv2(embed_input))).squeeze(3)\n",
    "        x3 = torch.tanh(self.dropout(self.conv3(embed_input))).squeeze(3)\n",
    "        f1 = self.mp1(x1).squeeze(2)\n",
    "        f2 = self.mp2(x2).squeeze(2)\n",
    "        f3 = self.mp3(x3).squeeze(2)\n",
    "        hidden = torch.cat([f1,f2,f3],dim=1)\n",
    "        logits = self.dense(hidden)\n",
    "        prediction = torch.sigmoid(logits)\n",
    "        target = target.view([-1,1])\n",
    "        correct_pred = torch.eq(torch.round(prediction).type(target.type()),target)\n",
    "        accuracy = torch.sum(correct_pred)\n",
    "        return prediction, accuracy\n",
    "   \n",
    "  \n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "epochs = 10\n",
    "batch_size = 50\n",
    "learning_rate = 0.001\n",
    "embed_size = 300\n",
    "seq_len = 1250\n",
    "dropout = 0.5\n",
    "filter_size = 100\n",
    "vocab_size = len(vocab_to_int)+1\n",
    "kernel_size = [3,4,5]\n",
    "model = CNN(vocab_size,embed_dims,filter_size,kernel_size,dropout,seq_len).to(device)\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)\n",
    "counter = 0\n",
    "CNN_acc = []\n",
    "CNN_valacc = []\n",
    "model.train()\n",
    "for e in range(epochs):\n",
    "  start_time = time.time()\n",
    "  for inputs, labels in train_loader:\n",
    "    \n",
    "    inputs, labels = inputs.cuda(), labels.cuda()\n",
    "    model.zero_grad()\n",
    "    logits, accuracy = model(inputs,labels)\n",
    "    loss = criterion(logits,labels.float())\n",
    "    loss.backward()\n",
    "    nn.utils.clip_grad_norm_(model.parameters(), 5)\n",
    "    optimizer.step()\n",
    "    if counter%10==0:\n",
    "        print(\"Epoch: {}/{}\".format(e,epochs),\n",
    "                     \"\\tIteration: {}\".format(counter),\n",
    "                     \"\\t\\tTrain Loss: {:.3f}\".format(loss.item()),\n",
    "                     \"\\tTrain Accuracy: {:.2f}\".format(accuracy.item()*100/batch_size))\n",
    "        CNN_acc.append(accuracy.item()*100/batch_size)\n",
    "    if counter%399==0:\n",
    "            with torch.no_grad():\n",
    "                model.eval()\n",
    "                val_acc = []\n",
    "                val_loss = []\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs_val, labels_val = inputs.cuda(), labels.cuda()\n",
    "                    logits_val,accuracy_val = model(inputs_val,labels_val)\n",
    "                    loss_val = criterion(logits_val,labels_val.float())\n",
    "                    val_acc.append(accuracy_val.item()*100/batch_size)\n",
    "                    val_loss.append(loss_val.item())\n",
    "                print(\"\\t\\tVal Loss: {:.3f}\".format(np.mean(val_loss)), \"\\t\\tVal Acc: {:.3f}\".format(np.mean(val_acc)))\n",
    "                CNN_valacc.append(np.mean(val_acc))\n",
    "                model.train()\n",
    "    counter += 1\n",
    "  print(\"Time to train epoch: {0} s\".format(time.time()-start_time))  \n",
    "with torch.no_grad():\n",
    "  model.eval()\n",
    "  test_acc = []\n",
    "  test_loss = []\n",
    "  for inputs, labels in test_loader:\n",
    "    input_test, labels_test = inputs.cuda(), labels.cuda()\n",
    "    logits_test,accuracy_test = model(input_test,labels_test)\n",
    "    loss_test = criterion(logits_test,labels_test.float())\n",
    "    test_acc.append(accuracy_test.item()*100/batch_size)\n",
    "    test_loss.append(loss_test.item())\n",
    "print(\"Test Loss: {:.3f}\".format(np.mean(test_loss)), \"\\tTest Acc: {:.3f}\".format(np.mean(test_acc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g0XCm3FLkiI1"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "iter = np.arange(0,4000,10)\n",
    "ep = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "d = {'QRNN':QRNN_acc,'1 Layer GRU':GRU_acc,'UniLSTM':UniLSTM_acc,'BiLSTM':BiLSTM_acc,'Multi Layer BiLSTM':MBiLSTM_acc,'CNN':CNN_acc}\n",
    "d2 = {'QRNN':QRNN_valacc,'1 Layer GRU':GRU_valacc,'UniLSTM':UniLSTM_valacc,'BiLSTM':BiLSTM_valacc,'Multi Layer BiLSTM':MBiLSTM_valacc,'CNN':CNN_valacc}\n",
    "df_acc = pd.DataFrame(data=d,index=iter)\n",
    "df_val = pd.DataFrame(data=d2,index=ep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "aSSnPUTLuc24",
    "outputId": "d8e83110-1ed9-49e6-aa40-79c41193ee41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab48f850f0>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aLwuxXqoyhIlCGAOBjWELauk4i0PDVGSYnEwg0wJrKMThsa2YEYV4pjX7pZDM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\n17FLX0e4/5fYtiFE9i2vpah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97qNwWGuQpi+hQFXUtgK72T5tHp8t7UAkSAitLRmYHcig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\nSDIQQggB/D9tSEB6c5dU7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab5e004e10>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fig,ax = plt.subplots()\n",
    "plt.plot(iter,QRNN_acc,label='QRNN')\n",
    "plt.plot(iter,GRU_acc,label='GRU')\n",
    "plt.plot(iter,UniLSTM_acc,label='UniLSTM')\n",
    "plt.plot(iter,BiLSTM_acc,label='BiLSTM')\n",
    "plt.plot(iter,MBiLSTM_acc,label='MBiLSTM')\n",
    "plt.plot(iter,CNN_acc,label='CNN')\n",
    "plt.title('Training Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "PHSwnqm-v7Hi",
    "outputId": "ccde1078-1052-439a-98e5-3f595863c9f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab455f39e8>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TKRjaVCG/QvRXgBD9tafwwvMMf/VLWKkUwR076fzkpwl0dM66rmzaNeGeFvOSl/pbrji/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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab47e6f128>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fig,ax = plt.subplots()\n",
    "plt.plot(ep,QRNN_valacc,label='QRNN')\n",
    "plt.plot(ep,GRU_valacc,label='GRU')\n",
    "plt.plot(ep,UniLSTM_valacc,label='UniLSTM')\n",
    "plt.plot(ep,BiLSTM_valacc,label='BiLSTM')\n",
    "plt.plot(ep,MBiLSTM_valacc,label='MBiLSTM')\n",
    "plt.plot(ep,CNN_valacc,label='CNN')\n",
    "plt.title('Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "uwBkn-DHzDMl",
    "outputId": "0d7f681c-101a-4c26-dab4-0d4a24713896"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab45259a90>"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JwFN5jimlrh6zjX98jT+j470eV0Knscvp4L3tq4gaURYFdstJjEIIMRLy3YF8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab4532f668>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e = [0,400,800,1200,1600,2000,2400,2800,3200,3600,4000]\n",
    "plt.plot(iter,QRNN_acc,label='Training')\n",
    "plt.plot(e,QRNN_valacc,label='Validation')\n",
    "plt.title('QRNN Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "ZUmNbKjyKpd-",
    "outputId": "3d585cc0-09b4-412a-862e-ba90fb3c6de6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab451ccb70>"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sagzDqRlEGcgRhh07G7usWjC7m7tp6ch38LV09Hkeh3ybeBjGrWkam3a1OwRJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab452c4588>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iter,GRU_acc,label='Training')\n",
    "plt.plot(e,GRU_valacc,label='Validation')\n",
    "plt.title('GRU Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "afiVw09JKpUW",
    "outputId": "dc41135d-d5de-423c-8668-6fefedf43295"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab451c0f98>"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kaWYWUL8EcznDqMiEAvChN20osHl/4ObzeGTXMa7dvBjDMFjamuDazYXhiK+5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab451e5748>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iter,UniLSTM_acc,label='Training')\n",
    "plt.plot(e,UniLSTM_valacc,label='Validation')\n",
    "plt.title('UniLSTM Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "01dNfHZRKpML",
    "outputId": "0db014bd-68a4-4403-f5a3-1fad9f627fad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab4535e208>"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FYp6x+NXj81VCDz83yr7DWZ7cPc4Tu8cYm84HJhsl4sEwUXiGr380XW2UmCsY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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab4515def0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iter,BiLSTM_acc,label='Training')\n",
    "plt.plot(e,BiLSTM_valacc,label='Validation')\n",
    "plt.title('BiLSTM Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "ABI3PKX4KpEY",
    "outputId": "3cfd96ba-89fc-4df5-c81b-ce2aaf3fe0d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab45430dd8>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tyYJSBgsAJ43ySEigbLYIW/jjLWWQK5Rdt4lpWj78VA1/tne2VyoZZAMz8nrN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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab45398e10>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iter,MBiLSTM_acc,label='Training')\n",
    "plt.plot(e,MBiLSTM_valacc,label='Validation')\n",
    "plt.title('Multi-Layer BiLSTM Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "id": "ixsLpR2hKoqI",
    "outputId": "c491014e-ea17-44a0-cbf6-50a7e0479d29"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fab4513b208>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VzlZsImOTzhgEAjrRcIB93aMYpllSlsAtTAeGvX916YyRb3vZ1hjBp2meQchK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fab45632630>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iter,CNN_acc,label='Training')\n",
    "plt.plot(e,CNN_valacc,label='Validation')\n",
    "plt.title('CNN Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h8Gre1RbSjFW"
   },
   "outputs": [],
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