text-classification/77.transfer-learning-albert-base.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://storage.googleapis.com/tfhub-modules/google/albert_base/2.tar.gz\n",
    "# !tar -zxf 2.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://raw.githubusercontent.com/huseinzol05/NLP-Models-Tensorflow/master/text-classification/data.zip\n",
    "# !unzip data.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://raw.githubusercontent.com/huseinzol05/NLP-Models-Tensorflow/master/text-classification/utils.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install albert-tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30k-clean.model  30k-clean.vocab  albert_config.json\r\n"
     ]
    }
   ],
   "source": [
    "!ls assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variables.data-00000-of-00001  variables.index\r\n"
     ]
    }
   ],
   "source": [
    "!ls variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/lamb_optimizer.py:34: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from albert import modeling\n",
    "from albert import optimization\n",
    "from albert import tokenization\n",
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/tokenization.py:240: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n",
      "\n",
      "INFO:tensorflow:loading sentence piece model\n"
     ]
    }
   ],
   "source": [
    "tokenizer = tokenization.FullTokenizer(\n",
    "      vocab_file='assets/30k-clean.vocab', do_lower_case=True,\n",
    "      spm_model_file='assets/30k-clean.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['▁hus', 'ein', '▁is', '▁so', '▁cute']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.tokenize('husein is so cute')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['negative', 'positive']\n",
      "10662\n",
      "10662\n"
     ]
    }
   ],
   "source": [
    "trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')\n",
    "trainset.data, trainset.target = separate_dataset(trainset,1.0)\n",
    "print (trainset.target_names)\n",
    "print (len(trainset.data))\n",
    "print (len(trainset.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_SEQ_LENGTH = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10662/10662 [00:00<00:00, 11143.98it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "input_ids, input_masks, segment_ids = [], [], []\n",
    "\n",
    "for text in tqdm(trainset.data):\n",
    "    tokens_a = tokenizer.tokenize(text.lower())\n",
    "    if len(tokens_a) > MAX_SEQ_LENGTH - 2:\n",
    "        tokens_a = tokens_a[:(MAX_SEQ_LENGTH - 2)]\n",
    "    tokens = [\"[CLS]\"] + tokens_a + [\"[SEP]\"]\n",
    "    segment_id = [0] * len(tokens)\n",
    "    input_id = tokenizer.convert_tokens_to_ids(tokens)\n",
    "    input_mask = [1] * len(input_id)\n",
    "    padding = [0] * (MAX_SEQ_LENGTH - len(input_id))\n",
    "    input_id += padding\n",
    "    input_mask += padding\n",
    "    segment_id += padding\n",
    "    \n",
    "    input_ids.append(input_id)\n",
    "    input_masks.append(input_mask)\n",
    "    segment_ids.append(segment_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:116: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<albert.modeling.AlbertConfig at 0x7f43c737be10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "albert_config = modeling.AlbertConfig.from_json_file('assets/albert_config.json')\n",
    "albert_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['▁moving', '▁uneven', '▁success']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.tokenize(trainset.data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch = 10\n",
    "batch_size = 32\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)"
   ]
  },
  {
   "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.int32, [None, None])\n",
    "        self.Y = tf.placeholder(tf.int32, [None])\n",
    "        \n",
    "        model = modeling.AlbertModel(\n",
    "            config=albert_config,\n",
    "            is_training=False,\n",
    "            input_ids=self.X,\n",
    "            input_mask=self.input_masks,\n",
    "            token_type_ids=self.segment_ids,\n",
    "            use_one_hot_embeddings=False)\n",
    "        \n",
    "        output_layer = model.get_pooled_output()\n",
    "        self.logits = tf.layers.dense(output_layer, dimension_output)\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 = optimization.create_optimizer(self.cost, learning_rate, \n",
    "                                                       num_train_steps, num_warmup_steps, False)\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/.local/lib/python3.6/site-packages/albert/modeling.py:194: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:507: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:588: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:1025: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/modeling.py:253: 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",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c639fb00>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c639fb00>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c639fb00>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c639fb00>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c737b940>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c737b940>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING: Entity <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c737b940>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Dense.call of <tensorflow.python.layers.core.Dense object at 0x7f43c737b940>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:36: 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/.local/lib/python3.6/site-packages/albert/optimization.py:41: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py:409: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "INFO:tensorflow:++++++ warmup starts at step 0, for 333 steps ++++++\n",
      "INFO:tensorflow:using adamw\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/albert/optimization.py:101: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1205: add_dispatch_support.<locals>.wrapper (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"
     ]
    }
   ],
   "source": [
    "dimension_output = 2\n",
    "learning_rate = 5e-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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Variable 'bert/embeddings/word_embeddings:0' shape=(30000, 128) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/embeddings/token_type_embeddings:0' shape=(2, 128) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/embeddings/position_embeddings:0' shape=(512, 128) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/embeddings/LayerNorm/beta:0' shape=(128,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/embeddings/LayerNorm/gamma:0' shape=(128,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/embedding_hidden_mapping_in/kernel:0' shape=(128, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/embedding_hidden_mapping_in/bias:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel:0' shape=(768, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/bias:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/kernel:0' shape=(768, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/bias:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/kernel:0' shape=(768, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/bias:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/kernel:0' shape=(768, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/bias:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm/beta:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm/gamma:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/kernel:0' shape=(768, 3072) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/bias:0' shape=(3072,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/kernel:0' shape=(3072, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/bias:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/beta:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/gamma:0' shape=(768,) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/pooler/dense/kernel:0' shape=(768, 768) dtype=float32_ref>,\n",
       " <tf.Variable 'bert/pooler/dense/bias:0' shape=(768,) dtype=float32_ref>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_lists = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = 'bert')\n",
    "saver = tf.train.Saver(var_list = var_lists)\n",
    "var_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/husein/.local/lib/python3.6/site-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from variables/variables\n"
     ]
    }
   ],
   "source": [
    "saver.restore(sess, 'variables/variables')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "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, trainset.target, test_size = 0.2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:59<00:00,  4.46it/s, accuracy=0.765, cost=0.51] \n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 12.26it/s, accuracy=0.667, cost=0.721]\n",
      "train minibatch loop:   0%|          | 0/267 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, pass acc: 0.000000, current acc: 0.737615\n",
      "time taken: 65.3192994594574\n",
      "epoch: 0, training loss: 0.588583, training acc: 0.693689, valid loss: 0.545611, valid acc: 0.737615\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.70it/s, accuracy=0.647, cost=0.495]\n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.28it/s, accuracy=0.714, cost=0.53] \n",
      "train minibatch loop:   0%|          | 0/267 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1, pass acc: 0.737615, current acc: 0.777711\n",
      "time taken: 61.85024404525757\n",
      "epoch: 1, training loss: 0.557109, training acc: 0.742843, valid loss: 0.498011, valid acc: 0.777711\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.71it/s, accuracy=0.941, cost=0.268]\n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.27it/s, accuracy=0.619, cost=0.792]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2, pass acc: 0.777711, current acc: 0.778157\n",
      "time taken: 61.79904627799988\n",
      "epoch: 2, training loss: 0.437586, training acc: 0.820040, valid loss: 0.557257, valid acc: 0.778157\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.71it/s, accuracy=0.882, cost=0.416] \n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.31it/s, accuracy=0.714, cost=0.909]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3, pass acc: 0.778157, current acc: 0.796933\n",
      "time taken: 61.780051469802856\n",
      "epoch: 3, training loss: 0.340006, training acc: 0.870001, valid loss: 0.635498, valid acc: 0.796933\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.70it/s, accuracy=0.941, cost=0.24]  \n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.28it/s, accuracy=0.714, cost=0.876]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 61.891632318496704\n",
      "epoch: 4, training loss: 0.279899, training acc: 0.908796, valid loss: 0.670878, valid acc: 0.795526\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.70it/s, accuracy=0.941, cost=0.249] \n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.26it/s, accuracy=0.762, cost=1.02] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 5, pass acc: 0.796933, current acc: 0.799053\n",
      "time taken: 61.81794476509094\n",
      "epoch: 5, training loss: 0.227987, training acc: 0.935997, valid loss: 0.876410, valid acc: 0.799053\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.70it/s, accuracy=0.941, cost=0.236] \n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.27it/s, accuracy=0.714, cost=1.08] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 61.82991981506348\n",
      "epoch: 6, training loss: 0.200658, training acc: 0.948308, valid loss: 0.842970, valid acc: 0.795057\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.71it/s, accuracy=0.941, cost=0.259] \n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 61.805060386657715\n",
      "epoch: 7, training loss: 0.169402, training acc: 0.958860, valid loss: 0.827865, valid acc: 0.791307\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train minibatch loop: 100%|██████████| 267/267 [00:56<00:00,  4.71it/s, accuracy=0.941, cost=0.169] \n",
      "test minibatch loop: 100%|██████████| 67/67 [00:05<00:00, 13.27it/s, accuracy=0.762, cost=1.04] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken: 61.79657506942749\n",
      "epoch: 8, training loss: 0.127402, training acc: 0.971992, valid loss: 0.854281, valid acc: 0.790615\n",
      "\n",
      "break epoch:9\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 = 3, 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 = 0, 0, 0, 0\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 += cost\n",
    "        train_acc += 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 += cost\n",
    "        test_acc += acc\n",
    "        pbar.set_postfix(cost = cost, accuracy = acc)\n",
    "\n",
    "    train_loss /= len(train_input_ids) / batch_size\n",
    "    train_acc /= len(train_input_ids) / batch_size\n",
    "    test_loss /= len(test_input_ids) / batch_size\n",
    "    test_acc /= len(test_input_ids) / batch_size\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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