{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/xlnet/model_utils.py:295: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import xlnet\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tqdm import tqdm\n",
    "import model_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prepro_utils import preprocess_text, encode_ids\n",
    "\n",
    "def tokenize_fn(text):\n",
    "    text = preprocess_text(text, lower= False)\n",
    "    return encode_ids(sp_model, text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sentencepiece as spm\n",
    "from prepro_utils import preprocess_text, encode_ids\n",
    "\n",
    "sp_model = spm.SentencePieceProcessor()\n",
    "sp_model.Load('xlnet-base/sp10m.cased.v9.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "\n",
    "left, right, label = [], [], []\n",
    "for file in glob.glob('../Malaya-Dataset/text-similarity/quora/*.json'):\n",
    "    with open(file) as fopen:\n",
    "        x = json.load(fopen)\n",
    "    for i in x:\n",
    "        splitted = i[0].split(' <> ')\n",
    "        if len(splitted) != 2:\n",
    "            continue\n",
    "        left.append(splitted[0])\n",
    "        right.append(splitted[1])\n",
    "        label.append(i[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = {'contradiction': 0, 'entailment': 1}\n",
    "\n",
    "snli = glob.glob('../malaya-dataset/Malaya-Dataset/text-similarity/snli/*.json')\n",
    "for file in snli:\n",
    "    with open(file) as fopen:\n",
    "        x = json.load(fopen)\n",
    "    for i in x:\n",
    "        splitted = i[1].split(' <> ')\n",
    "        if len(splitted) != 2:\n",
    "            continue\n",
    "        if not l.get(i[0]):\n",
    "            continue\n",
    "        left.append(splitted[0])\n",
    "        right.append(splitted[1])\n",
    "        try:\n",
    "            label.append(l[i[0]])\n",
    "        except:\n",
    "            print(splitted, i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEG_ID_A   = 0\n",
    "SEG_ID_B   = 1\n",
    "SEG_ID_CLS = 2\n",
    "SEG_ID_SEP = 3\n",
    "SEG_ID_PAD = 4\n",
    "\n",
    "special_symbols = {\n",
    "    \"<unk>\"  : 0,\n",
    "    \"<s>\"    : 1,\n",
    "    \"</s>\"   : 2,\n",
    "    \"<cls>\"  : 3,\n",
    "    \"<sep>\"  : 4,\n",
    "    \"<pad>\"  : 5,\n",
    "    \"<mask>\" : 6,\n",
    "    \"<eod>\"  : 7,\n",
    "    \"<eop>\"  : 8,\n",
    "}\n",
    "\n",
    "VOCAB_SIZE = 32000\n",
    "UNK_ID = special_symbols[\"<unk>\"]\n",
    "CLS_ID = special_symbols[\"<cls>\"]\n",
    "SEP_ID = special_symbols[\"<sep>\"]\n",
    "MASK_ID = special_symbols[\"<mask>\"]\n",
    "EOD_ID = special_symbols[\"<eod>\"]\n",
    "MAX_SEQ_LENGTH = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _truncate_seq_pair(tokens_a, tokens_b, max_length):\n",
    "    while True:\n",
    "        total_length = len(tokens_a) + len(tokens_b)\n",
    "        if total_length <= max_length:\n",
    "            break\n",
    "        if len(tokens_a) > len(tokens_b):\n",
    "              tokens_a.pop()\n",
    "        else:\n",
    "              tokens_b.pop()\n",
    "                \n",
    "def get_inputs(left, right):\n",
    "\n",
    "    input_ids, input_mask, all_seg_ids = [], [], []\n",
    "    \n",
    "    for i in tqdm(range(len(left))):\n",
    "        tokens = tokenize_fn(left[i])\n",
    "        tokens_right = tokenize_fn(right[i])\n",
    "        _truncate_seq_pair(tokens, tokens_right, MAX_SEQ_LENGTH - 3)\n",
    "        \n",
    "        segment_ids = [SEG_ID_A] * len(tokens)\n",
    "        tokens.append(SEP_ID)\n",
    "        segment_ids.append(SEG_ID_A)\n",
    "\n",
    "        tokens.extend(tokens_right)\n",
    "        segment_ids.extend([SEG_ID_B] * len(tokens_right))\n",
    "        tokens.append(SEP_ID)\n",
    "        segment_ids.append(SEG_ID_B)\n",
    "\n",
    "        tokens.append(CLS_ID)\n",
    "        segment_ids.append(SEG_ID_CLS)\n",
    "        \n",
    "        cur_input_ids = tokens\n",
    "        cur_input_mask = [0] * len(cur_input_ids)\n",
    "        input_ids.append(tokens)\n",
    "        input_mask.append(cur_input_mask)\n",
    "        all_seg_ids.append(segment_ids)\n",
    "        \n",
    "    return input_ids, input_mask, all_seg_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 593596/593596 [01:34<00:00, 6279.85it/s]\n"
     ]
    }
   ],
   "source": [
    "input_ids, input_masks, segment_ids = get_inputs(left, right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "input_ids = pad_sequences(input_ids,padding='post')\n",
    "input_masks = pad_sequences(input_masks,padding='post', value = 1)\n",
    "segment_ids = pad_sequences(segment_ids,padding='post', value = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((593596, 100), (593596, 100), (593596, 100), 593596)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_ids.shape, input_masks.shape, segment_ids.shape, len(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/xlnet/xlnet.py:63: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kwargs = dict(\n",
    "      is_training=True,\n",
    "      use_tpu=False,\n",
    "      use_bfloat16=False,\n",
    "      dropout=0.1,\n",
    "      dropatt=0.1,\n",
    "      init='normal',\n",
    "      init_range=0.1,\n",
    "      init_std=0.05,\n",
    "      clamp_len=-1)\n",
    "\n",
    "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
    "xlnet_config = xlnet.XLNetConfig(json_path='xlnet-base/config.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197865 19786\n"
     ]
    }
   ],
   "source": [
    "epoch = 20\n",
    "batch_size = 60\n",
    "warmup_proportion = 0.1\n",
    "num_train_steps = int(len(input_ids) / batch_size * epoch)\n",
    "num_warmup_steps = int(num_train_steps * warmup_proportion)\n",
    "print(num_train_steps, num_warmup_steps)\n",
    "learning_rate = 2e-5\n",
    "\n",
    "training_parameters = dict(\n",
    "      decay_method = 'poly',\n",
    "      train_steps = num_train_steps,\n",
    "      learning_rate = learning_rate,\n",
    "      warmup_steps = num_warmup_steps,\n",
    "      min_lr_ratio = 0.0,\n",
    "      weight_decay = 0.00,\n",
    "      adam_epsilon = 1e-8,\n",
    "      num_core_per_host = 1,\n",
    "      lr_layer_decay_rate = 1,\n",
    "      use_tpu=False,\n",
    "      use_bfloat16=False,\n",
    "      dropout=0.0,\n",
    "      dropatt=0.0,\n",
    "      init='normal',\n",
    "      init_range=0.1,\n",
    "      init_std=0.05,\n",
    "      clip = 1.0,\n",
    "      clamp_len=-1,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameter:\n",
    "    def __init__(self, decay_method, warmup_steps, weight_decay, adam_epsilon, \n",
    "                num_core_per_host, lr_layer_decay_rate, use_tpu, learning_rate, train_steps,\n",
    "                min_lr_ratio, clip, **kwargs):\n",
    "        self.decay_method = decay_method\n",
    "        self.warmup_steps = warmup_steps\n",
    "        self.weight_decay = weight_decay\n",
    "        self.adam_epsilon = adam_epsilon\n",
    "        self.num_core_per_host = num_core_per_host\n",
    "        self.lr_layer_decay_rate = lr_layer_decay_rate\n",
    "        self.use_tpu = use_tpu\n",
    "        self.learning_rate = learning_rate\n",
    "        self.train_steps = train_steps\n",
    "        self.min_lr_ratio = min_lr_ratio\n",
    "        self.clip = clip\n",
    "        \n",
    "training_parameters = Parameter(**training_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        dimension_output,\n",
    "        learning_rate = 2e-5,\n",
    "    ):\n",
    "        self.X = tf.placeholder(tf.int32, [None, None])\n",
    "        self.segment_ids = tf.placeholder(tf.int32, [None, None])\n",
    "        self.input_masks = tf.placeholder(tf.float32, [None, None])\n",
    "        self.Y = tf.placeholder(tf.int32, [None])\n",
    "        \n",
    "        xlnet_model = xlnet.XLNetModel(\n",
    "            xlnet_config=xlnet_config,\n",
    "            run_config=xlnet_parameters,\n",
    "            input_ids=tf.transpose(self.X, [1, 0]),\n",
    "            seg_ids=tf.transpose(self.segment_ids, [1, 0]),\n",
    "            input_mask=tf.transpose(self.input_masks, [1, 0]))\n",
    "        \n",
    "        summary = xlnet_model.get_pooled_out(\"last\", True)\n",
    "        print(summary)\n",
    "        \n",
    "        self.logits = tf.layers.dense(summary, dimension_output)\n",
    "        self.logits = tf.identity(self.logits, name = 'logits')\n",
    "        \n",
    "        self.cost = tf.reduce_mean(\n",
    "            tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
    "                logits = self.logits, labels = self.Y\n",
    "            )\n",
    "        )\n",
    "        \n",
    "        self.optimizer, self.learning_rate, _ = model_utils.get_train_op(training_parameters, self.cost)\n",
    "        \n",
    "        correct_pred = tf.equal(\n",
    "            tf.argmax(self.logits, 1, output_type = tf.int32), self.Y\n",
    "        )\n",
    "        self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/xlnet/xlnet.py:220: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/xlnet/xlnet.py:220: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:453: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
      "\n",
      "INFO:tensorflow:memory input None\n",
      "INFO:tensorflow:Use float type <dtype: 'float32'>\n",
      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:460: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:535: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/layers/core.py:271: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/xlnet/modeling.py:67: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.Dense instead.\n",
      "Tensor(\"model_1/sequnece_summary/dropout/dropout/mul_1:0\", shape=(?, 768), dtype=float32)\n",
      "WARNING:tensorflow:From /home/husein/xlnet/model_utils.py:96: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/xlnet/model_utils.py:108: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/xlnet/model_utils.py:123: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /home/husein/xlnet/model_utils.py:131: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dimension_output = 2\n",
    "\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "model = Model(\n",
    "    dimension_output,\n",
    "    learning_rate\n",
    ")\n",
    "\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import re\n",
    "\n",
    "def get_assignment_map_from_checkpoint(tvars, init_checkpoint):\n",
    "    \"\"\"Compute the union of the current variables and checkpoint variables.\"\"\"\n",
    "    assignment_map = {}\n",
    "    initialized_variable_names = {}\n",
    "\n",
    "    name_to_variable = collections.OrderedDict()\n",
    "    for var in tvars:\n",
    "        name = var.name\n",
    "        m = re.match('^(.*):\\\\d+$', name)\n",
    "        if m is not None:\n",
    "            name = m.group(1)\n",
    "        name_to_variable[name] = var\n",
    "\n",
    "    init_vars = tf.train.list_variables(init_checkpoint)\n",
    "\n",
    "    assignment_map = collections.OrderedDict()\n",
    "    for x in init_vars:\n",
    "        (name, var) = (x[0], x[1])\n",
    "        if name not in name_to_variable:\n",
    "            continue\n",
    "        assignment_map[name] = name_to_variable[name]\n",
    "        initialized_variable_names[name] = 1\n",
    "        initialized_variable_names[name + ':0'] = 1\n",
    "\n",
    "    return (assignment_map, initialized_variable_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvars = tf.trainable_variables()\n",
    "checkpoint = 'xlnet-base/model.ckpt'\n",
    "assignment_map, initialized_variable_names = get_assignment_map_from_checkpoint(tvars, \n",
    "                                                                                checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_input_ids, test_input_ids, train_input_masks, test_input_masks, train_segment_ids, test_segment_ids, train_Y, test_Y = train_test_split(\n",
    "    input_ids, input_masks, segment_ids, label, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 7915/7915 [1:19:15<00:00,  1.66it/s, accuracy=0.75, cost=0.416] \n",
      "test minibatch loop: 100%|██████████| 1979/1979 [07:05<00:00,  4.65it/s, accuracy=0.65, cost=0.607] \n",
      "train minibatch loop:   0%|          | 0/7915 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, pass acc: 0.000000, current acc: 0.788917\n",
      "time taken: 5181.4275097846985\n",
      "epoch: 0, training loss: 0.465134, training acc: 0.753531, valid loss: 0.396933, valid acc: 0.788917\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 7915/7915 [1:19:17<00:00,  1.66it/s, accuracy=0.833, cost=0.415]\n",
      "test minibatch loop: 100%|██████████| 1979/1979 [07:04<00:00,  4.66it/s, accuracy=0.65, cost=0.508] \n",
      "train minibatch loop:   0%|          | 0/7915 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1, pass acc: 0.788917, current acc: 0.803798\n",
      "time taken: 5182.63897895813\n",
      "epoch: 1, training loss: 0.369613, training acc: 0.816349, valid loss: 0.369464, valid acc: 0.803798\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 7915/7915 [1:19:08<00:00,  1.67it/s, accuracy=0.833, cost=0.349]\n",
      "test minibatch loop: 100%|██████████| 1979/1979 [07:03<00:00,  4.68it/s, accuracy=0.725, cost=0.438]\n",
      "train minibatch loop:   0%|          | 0/7915 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2, pass acc: 0.803798, current acc: 0.834997\n",
      "time taken: 5171.688594818115\n",
      "epoch: 2, training loss: 0.336626, training acc: 0.838042, valid loss: 0.340342, valid acc: 0.834997\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 7915/7915 [1:19:06<00:00,  1.67it/s, accuracy=0.861, cost=0.401]\n",
      "test minibatch loop: 100%|██████████| 1979/1979 [07:03<00:00,  4.68it/s, accuracy=0.825, cost=0.322]\n",
      "train minibatch loop:   0%|          | 0/7915 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3, pass acc: 0.834997, current acc: 0.849196\n",
      "time taken: 5169.649661540985\n",
      "epoch: 3, training loss: 0.305056, training acc: 0.856933, valid loss: 0.320067, valid acc: 0.849196\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 7915/7915 [1:19:10<00:00,  1.67it/s, accuracy=0.861, cost=0.325] \n",
      "test minibatch loop: 100%|██████████| 1979/1979 [07:06<00:00,  4.64it/s, accuracy=0.8, cost=0.359]  \n",
      "train minibatch loop:   0%|          | 0/7915 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 4, pass acc: 0.849196, current acc: 0.854792\n",
      "time taken: 5177.5598912239075\n",
      "epoch: 4, training loss: 0.278160, training acc: 0.873619, valid loss: 0.314210, valid acc: 0.854792\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 7915/7915 [1:19:10<00:00,  1.67it/s, accuracy=0.944, cost=0.147] \n",
      "test minibatch loop: 100%|██████████| 1979/1979 [07:07<00:00,  4.63it/s, accuracy=0.825, cost=0.317] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 5177.579535007477\n",
      "epoch: 5, training loss: 0.254044, training acc: 0.886641, valid loss: 0.322444, valid acc: 0.854704\n",
      "\n",
      "break epoch:6\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "EARLY_STOPPING, CURRENT_CHECKPOINT, CURRENT_ACC, EPOCH = 1, 0, 0, 0\n",
    "\n",
    "while True:\n",
    "    lasttime = time.time()\n",
    "    if CURRENT_CHECKPOINT == EARLY_STOPPING:\n",
    "        print('break epoch:%d\\n' % (EPOCH))\n",
    "        break\n",
    "\n",
    "    train_acc, train_loss, test_acc, test_loss = [], [], [], []\n",
    "    pbar = tqdm(\n",
    "        range(0, len(train_input_ids), batch_size), desc = 'train minibatch loop'\n",
    "    )\n",
    "    for i in pbar:\n",
    "        index = min(i + batch_size, len(train_input_ids))\n",
    "        batch_x = train_input_ids[i: index]\n",
    "        batch_masks = train_input_masks[i: index]\n",
    "        batch_segment = train_segment_ids[i: index]\n",
    "        batch_y = train_Y[i: index]\n",
    "        acc, cost, _ = sess.run(\n",
    "            [model.accuracy, model.cost, model.optimizer],\n",
    "            feed_dict = {\n",
    "                model.Y: batch_y,\n",
    "                model.X: batch_x,\n",
    "                model.segment_ids: batch_segment,\n",
    "                model.input_masks: batch_masks\n",
    "            },\n",
    "        )\n",
    "        assert not np.isnan(cost)\n",
    "        train_loss.append(cost)\n",
    "        train_acc.append(acc)\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "        \n",
    "    pbar = tqdm(range(0, len(test_input_ids), batch_size), desc = 'test minibatch loop')\n",
    "    for i in pbar:\n",
    "        index = min(i + batch_size, len(test_input_ids))\n",
    "        batch_x = test_input_ids[i: index]\n",
    "        batch_masks = test_input_masks[i: index]\n",
    "        batch_segment = test_segment_ids[i: index]\n",
    "        batch_y = test_Y[i: index]\n",
    "        acc, cost = sess.run(\n",
    "            [model.accuracy, model.cost],\n",
    "            feed_dict = {\n",
    "                model.Y: batch_y,\n",
    "                model.X: batch_x,\n",
    "                model.segment_ids: batch_segment,\n",
    "                model.input_masks: batch_masks\n",
    "            },\n",
    "        )\n",
    "        test_loss.append(cost)\n",
    "        test_acc.append(acc)\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "\n",
    "    train_loss = np.mean(train_loss)\n",
    "    train_acc = np.mean(train_acc)\n",
    "    test_loss = np.mean(test_loss)\n",
    "    test_acc = np.mean(test_acc)\n",
    "\n",
    "    if test_acc > CURRENT_ACC:\n",
    "        print(\n",
    "            'epoch: %d, pass acc: %f, current acc: %f'\n",
    "            % (EPOCH, CURRENT_ACC, test_acc)\n",
    "        )\n",
    "        CURRENT_ACC = test_acc\n",
    "        CURRENT_CHECKPOINT = 0\n",
    "    else:\n",
    "        CURRENT_CHECKPOINT += 1\n",
    "        \n",
    "    print('time taken:', time.time() - lasttime)\n",
    "    print(\n",
    "        'epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f\\n'\n",
    "        % (EPOCH, train_loss, train_acc, test_loss, test_acc)\n",
    "    )\n",
    "    EPOCH += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xlnet-base-similarity/model.ckpt'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saver = tf.train.Saver(tf.trainable_variables())\n",
    "saver.save(sess, 'xlnet-base-similarity/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "kwargs = dict(\n",
    "      is_training=False,\n",
    "      use_tpu=False,\n",
    "      use_bfloat16=False,\n",
    "      dropout=0.0,\n",
    "      dropatt=0.0,\n",
    "      init='normal',\n",
    "      init_range=0.1,\n",
    "      init_std=0.05,\n",
    "      clamp_len=-1)\n",
    "\n",
    "xlnet_parameters = xlnet.RunConfig(**kwargs)\n",
    "xlnet_config = xlnet.XLNetConfig(json_path='xlnet-base/config.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:memory input None\n",
      "INFO:tensorflow:Use float type <dtype: 'float32'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"model_1/sequnece_summary/dropout/Identity:0\", shape=(?, 768), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "dimension_output = 2\n",
    "learning_rate = 2e-5\n",
    "\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "model = Model(\n",
    "    dimension_output,\n",
    "    learning_rate\n",
    ")\n",
    "\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from xlnet-base-similarity/model.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver = tf.train.Saver(tf.trainable_variables())\n",
    "saver.restore(sess, 'xlnet-base-similarity/model.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "validation minibatch loop: 100%|██████████| 1979/1979 [06:04<00:00,  5.42it/s]\n"
     ]
    }
   ],
   "source": [
    "real_Y, predict_Y = [], []\n",
    "\n",
    "pbar = tqdm(\n",
    "    range(0, len(test_input_ids), batch_size), desc = 'validation minibatch loop'\n",
    ")\n",
    "for i in pbar:\n",
    "    index = min(i + batch_size, len(test_input_ids))\n",
    "    batch_x = test_input_ids[i: index]\n",
    "    batch_masks = test_input_masks[i: index]\n",
    "    batch_segment = test_segment_ids[i: index]\n",
    "    batch_y = test_Y[i: index]\n",
    "    predict_Y += np.argmax(sess.run(model.logits,\n",
    "            feed_dict = {\n",
    "                model.Y: batch_y,\n",
    "                model.X: batch_x,\n",
    "                model.segment_ids: batch_segment,\n",
    "                model.input_masks: batch_masks\n",
    "            },\n",
    "    ), 1, ).tolist()\n",
    "    real_Y += batch_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      " not similar    0.77288   0.94355   0.84973     51004\n",
      "     similar    0.94900   0.79116   0.86292     67716\n",
      "\n",
      "    accuracy                        0.85663    118720\n",
      "   macro avg    0.86094   0.86736   0.85633    118720\n",
      "weighted avg    0.87334   0.85663   0.85725    118720\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print(\n",
    "    metrics.classification_report(\n",
    "        real_Y, predict_Y, target_names = ['not similar', 'similar'],\n",
    "        digits = 5\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Placeholder',\n",
       " 'Placeholder_1',\n",
       " 'Placeholder_2',\n",
       " 'Placeholder_3',\n",
       " 'model/transformer/r_w_bias',\n",
       " 'model/transformer/r_r_bias',\n",
       " 'model/transformer/word_embedding/lookup_table',\n",
       " 'model/transformer/r_s_bias',\n",
       " 'model/transformer/seg_embed',\n",
       " 'model/transformer/layer_0/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_0/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_0/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_0/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_0/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_0/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_0/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_0/ff/layer_1/bias',\n",
       " 'model/transformer/layer_0/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_0/ff/layer_2/bias',\n",
       " 'model/transformer/layer_0/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_1/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_1/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_1/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_1/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_1/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_1/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_1/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_1/ff/layer_1/bias',\n",
       " 'model/transformer/layer_1/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_1/ff/layer_2/bias',\n",
       " 'model/transformer/layer_1/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_2/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_2/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_2/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_2/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_2/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_2/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_2/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_2/ff/layer_1/bias',\n",
       " 'model/transformer/layer_2/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_2/ff/layer_2/bias',\n",
       " 'model/transformer/layer_2/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_3/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_3/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_3/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_3/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_3/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_3/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_3/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_3/ff/layer_1/bias',\n",
       " 'model/transformer/layer_3/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_3/ff/layer_2/bias',\n",
       " 'model/transformer/layer_3/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_4/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_4/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_4/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_4/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_4/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_4/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_4/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_4/ff/layer_1/bias',\n",
       " 'model/transformer/layer_4/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_4/ff/layer_2/bias',\n",
       " 'model/transformer/layer_4/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_5/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_5/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_5/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_5/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_5/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_5/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_5/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_5/ff/layer_1/bias',\n",
       " 'model/transformer/layer_5/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_5/ff/layer_2/bias',\n",
       " 'model/transformer/layer_5/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_6/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_6/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_6/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_6/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_6/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_6/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_6/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_6/ff/layer_1/bias',\n",
       " 'model/transformer/layer_6/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_6/ff/layer_2/bias',\n",
       " 'model/transformer/layer_6/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_7/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_7/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_7/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_7/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_7/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_7/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_7/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_7/ff/layer_1/bias',\n",
       " 'model/transformer/layer_7/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_7/ff/layer_2/bias',\n",
       " 'model/transformer/layer_7/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_8/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_8/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_8/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_8/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_8/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_8/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_8/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_8/ff/layer_1/bias',\n",
       " 'model/transformer/layer_8/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_8/ff/layer_2/bias',\n",
       " 'model/transformer/layer_8/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_9/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_9/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_9/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_9/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_9/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_9/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_9/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_9/ff/layer_1/bias',\n",
       " 'model/transformer/layer_9/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_9/ff/layer_2/bias',\n",
       " 'model/transformer/layer_9/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_10/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_10/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_10/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_10/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_10/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_10/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_10/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_10/ff/layer_1/bias',\n",
       " 'model/transformer/layer_10/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_10/ff/layer_2/bias',\n",
       " 'model/transformer/layer_10/ff/LayerNorm/gamma',\n",
       " 'model/transformer/layer_11/rel_attn/q/kernel',\n",
       " 'model/transformer/layer_11/rel_attn/k/kernel',\n",
       " 'model/transformer/layer_11/rel_attn/v/kernel',\n",
       " 'model/transformer/layer_11/rel_attn/r/kernel',\n",
       " 'model/transformer/layer_11/rel_attn/o/kernel',\n",
       " 'model/transformer/layer_11/rel_attn/LayerNorm/gamma',\n",
       " 'model/transformer/layer_11/ff/layer_1/kernel',\n",
       " 'model/transformer/layer_11/ff/layer_1/bias',\n",
       " 'model/transformer/layer_11/ff/layer_2/kernel',\n",
       " 'model/transformer/layer_11/ff/layer_2/bias',\n",
       " 'model/transformer/layer_11/ff/LayerNorm/gamma',\n",
       " 'model/sequnece_summary/summary/kernel',\n",
       " 'model/sequnece_summary/summary/bias',\n",
       " 'dense/kernel',\n",
       " 'dense/bias',\n",
       " 'logits']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strings = ','.join(\n",
    "    [\n",
    "        n.name\n",
    "        for n in tf.get_default_graph().as_graph_def().node\n",
    "        if ('Variable' in n.op\n",
    "        or 'Placeholder' in n.name\n",
    "        or 'logits' in n.name\n",
    "        or 'alphas' in n.name\n",
    "        or 'self/Softmax' in n.name)\n",
    "        and 'Adam' not in n.name\n",
    "        and 'beta' not in n.name\n",
    "        and 'global_step' not in n.name\n",
    "    ]\n",
    ")\n",
    "strings.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def freeze_graph(model_dir, output_node_names):\n",
    "\n",
    "    if not tf.gfile.Exists(model_dir):\n",
    "        raise AssertionError(\n",
    "            \"Export directory doesn't exists. Please specify an export \"\n",
    "            'directory: %s' % model_dir\n",
    "        )\n",
    "\n",
    "    checkpoint = tf.train.get_checkpoint_state(model_dir)\n",
    "    input_checkpoint = checkpoint.model_checkpoint_path\n",
    "\n",
    "    absolute_model_dir = '/'.join(input_checkpoint.split('/')[:-1])\n",
    "    output_graph = absolute_model_dir + '/frozen_model.pb'\n",
    "    clear_devices = True\n",
    "    with tf.Session(graph = tf.Graph()) as sess:\n",
    "        saver = tf.train.import_meta_graph(\n",
    "            input_checkpoint + '.meta', clear_devices = clear_devices\n",
    "        )\n",
    "        saver.restore(sess, input_checkpoint)\n",
    "        output_graph_def = tf.graph_util.convert_variables_to_constants(\n",
    "            sess,\n",
    "            tf.get_default_graph().as_graph_def(),\n",
    "            output_node_names.split(','),\n",
    "        )\n",
    "        with tf.gfile.GFile(output_graph, 'wb') as f:\n",
    "            f.write(output_graph_def.SerializeToString())\n",
    "        print('%d ops in the final graph.' % len(output_graph_def.node))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from xlnet-base-similarity/model.ckpt\n",
      "WARNING:tensorflow:From <ipython-input-30-9a7215a4e58a>:23: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
      "INFO:tensorflow:Froze 165 variables.\n",
      "INFO:tensorflow:Converted 165 variables to const ops.\n",
      "7669 ops in the final graph.\n"
     ]
    }
   ],
   "source": [
    "freeze_graph('xlnet-base-similarity', strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n"
     ]
    }
   ],
   "source": [
    "def load_graph(frozen_graph_filename):\n",
    "    with tf.gfile.GFile(frozen_graph_filename, 'rb') as f:\n",
    "        graph_def = tf.GraphDef()\n",
    "        graph_def.ParseFromString(f.read())\n",
    "    with tf.Graph().as_default() as graph:\n",
    "        tf.import_graph_def(graph_def)\n",
    "    return graph\n",
    "\n",
    "g = load_graph('xlnet-base-similarity/frozen_model.pb')\n",
    "x = g.get_tensor_by_name('import/Placeholder:0')\n",
    "seg = g.get_tensor_by_name('import/Placeholder_1:0')\n",
    "m = g.get_tensor_by_name('import/Placeholder_2:0')\n",
    "logits = g.get_tensor_by_name('import/logits:0')\n",
    "test_sess = tf.InteractiveSession(graph = g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.5434387 , -2.7328088 ],\n",
       "       [-4.7541265 ,  5.163885  ],\n",
       "       [-5.470607  ,  4.3164625 ],\n",
       "       [-4.553798  ,  5.1717024 ],\n",
       "       [-1.9533242 ,  0.44452572],\n",
       "       [-4.439697  ,  5.793971  ],\n",
       "       [-0.2911216 , -0.0261218 ],\n",
       "       [-4.7054543 ,  4.7711077 ],\n",
       "       [ 0.28674954, -1.8945521 ],\n",
       "       [-1.6190811 ,  0.5553683 ],\n",
       "       [ 0.13827887, -3.5472271 ],\n",
       "       [ 0.5268437 , -2.6319551 ],\n",
       "       [-0.9359468 ,  1.0296911 ],\n",
       "       [ 2.0339055 , -2.1630902 ],\n",
       "       [ 0.9112381 ,  0.58463794],\n",
       "       [-1.0450312 ,  0.10713197],\n",
       "       [-5.4646316 ,  4.3009014 ],\n",
       "       [ 1.4644347 , -2.5155988 ],\n",
       "       [ 1.329641  , -2.7189822 ],\n",
       "       [-0.62557185, -0.08425825],\n",
       "       [ 2.237051  , -1.6611747 ],\n",
       "       [-0.09839378, -0.66356647],\n",
       "       [-1.615195  ,  1.8174384 ],\n",
       "       [ 1.3267742 , -2.6401014 ],\n",
       "       [ 1.2640498 ,  0.7178159 ],\n",
       "       [ 1.6135963 , -2.1411867 ],\n",
       "       [ 2.2238655 , -2.0346007 ],\n",
       "       [-4.353797  ,  4.63577   ],\n",
       "       [-5.0723886 ,  4.989188  ],\n",
       "       [-0.2743207 ,  0.04308655],\n",
       "       [-0.11212153,  0.19780046],\n",
       "       [ 0.52981037, -2.7525704 ],\n",
       "       [ 0.37943932, -2.0910525 ],\n",
       "       [-0.9020747 ,  0.6957007 ],\n",
       "       [-2.5029292 ,  1.4442266 ],\n",
       "       [ 0.82339716, -2.6325045 ],\n",
       "       [-1.699767  ,  0.2745518 ],\n",
       "       [-0.7400208 ,  0.45596293],\n",
       "       [ 2.4343715 , -1.9416034 ],\n",
       "       [ 1.1341429 , -0.0840399 ]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_sess.run(logits, feed_dict = {x: batch_x,\n",
    "                             seg: batch_segment,\n",
    "                             m: batch_masks})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "\n",
    "bucketName = 'huseinhouse-storage'\n",
    "Key = 'xlnet-base-similarity/frozen_model.pb'\n",
    "outPutname = \"v34/similarity/xlnet-base-similarity.pb\"\n",
    "\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "s3.upload_file(Key,bucketName,outPutname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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