In [1]:
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow2/text_matching/snli/main')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
In [2]:
%tensorflow_version 2.x
!pip install tensorflow-addons
!pip install transformers
Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.6/dist-packages (0.8.3)
Requirement already satisfied: typeguard in /usr/local/lib/python3.6/dist-packages (from tensorflow-addons) (2.7.1)
Requirement already satisfied: transformers in /usr/local/lib/python3.6/dist-packages (3.0.2)
Requirement already satisfied: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers) (0.0.43)
Requirement already satisfied: dataclasses; python_version < "3.7" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)
Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)
Requirement already satisfied: tokenizers==0.8.1.rc1 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8.1rc1)
Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.18.5)
Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)
Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)
Requirement already satisfied: sentencepiece!=0.1.92 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.1.91)
Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.16.0)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.12.0)
Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.6.20)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)
Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)
In [3]:
from transformers import BertTokenizer, TFBertModel

import tensorflow as tf
import tensorflow_addons as tfa
import numpy as np
import pprint
import logging
import time

print("TensorFlow Version", tf.__version__)
print('GPU Enabled:', tf.test.is_gpu_available())
TensorFlow Version 2.2.0
WARNING:tensorflow:From <ipython-input-3-0175c78ad17c>:11: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
GPU Enabled: True
In [4]:
params = {
  'train_path': '../data/train.txt',
  'test_path': '../data/test.txt',
  'pretrain_path': 'bert-base-uncased',
  'num_samples': 550152,
  'buffer_size': 200000,
  'batch_size': 32,
  'max_len': 128 + 3,
  'num_patience': 5,
  'init_lr': 1e-5,
  'max_lr': 3e-5,
}
In [5]:
tokenizer = BertTokenizer.from_pretrained(params['pretrain_path'],
                                          lowercase = True,
                                          add_special_tokens = True)
In [6]:
# stream data from text files
def data_generator(f_path, params):
  label2idx = {'neutral': 0, 'entailment': 1, 'contradiction': 2,}
  with open(f_path) as f:
    print('Reading', f_path)
    for line in f:
      line = line.rstrip()
      label, text1, text2 = line.split('\t')
      if label == '-':
        continue
      text1 = tokenizer.tokenize(text1)
      text2 = tokenizer.tokenize(text2)
      if len(text1) + len(text2) + 3 > params['max_len']:
        _max_len = (params['max_len'] - 3) // 2
        text1 = text1[:_max_len]
        text2 = text2[:_max_len]
      text = ['[CLS]'] + text1 + ['[SEP]'] + text2 + ['[SEP]']
      text = tokenizer.convert_tokens_to_ids(text)
      seg = [0] + [0] * len(text1) + [0] + [1] * len(text2) + [1]
      yield text, seg, label2idx[label]


def dataset(is_training, params):
  _shapes = ([None], [None], ())
  _types = (tf.int32, tf.int32, tf.int32)
  _pads = (0, 0, -1)
  
  if is_training:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['train_path'], params),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.shuffle(params['buffer_size'])
    ds = ds.padded_batch(params['batch_size'], _shapes, _pads)
    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
  else:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['test_path'], params),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.padded_batch(params['batch_size'], _shapes, _pads)
    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
  
  return ds
In [7]:
# input stream ids check
text, seg, _ = next(data_generator(params['train_path'], params))
print(text)
print(seg)
Reading ../data/train.txt
[101, 1037, 2711, 2006, 1037, 3586, 14523, 2058, 1037, 3714, 2091, 13297, 102, 1037, 2711, 2003, 2731, 2010, 3586, 2005, 1037, 2971, 102]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
In [8]:
class BertFinetune(tf.keras.Model):
  def __init__(self, params):
    super(BertFinetune, self).__init__()
    self.bert = TFBertModel.from_pretrained(params['pretrain_path'],
                                            trainable = True)
    self.drop_1 = tf.keras.layers.Dropout(.1)
    self.fc = tf.keras.layers.Dense(300, tf.nn.swish, name='down_stream/fc')
    self.drop_2 = tf.keras.layers.Dropout(.1)
    self.out = tf.keras.layers.Dense(3, name='down_stream/out')

  def call(self, bert_inputs, training):
    bert_inputs = [tf.cast(inp, tf.int32) for inp in bert_inputs]
    x = self.bert(bert_inputs, training=training)[1]
    x = self.drop_1(x, training=training)
    x = self.fc(x)
    x = self.drop_2(x, training=training)
    x = self.out(x)
    return x
In [ ]:
model = BertFinetune(params)
model.build([[None, None], [None, None], [None, None]])
pprint.pprint([(v.name, v.shape) for v in model.trainable_variables])

step_size = 2 * params['num_samples'] // params['batch_size']
decay_lr = tfa.optimizers.Triangular2CyclicalLearningRate(
  initial_learning_rate = params['init_lr'],
  maximal_learning_rate = params['max_lr'],
  step_size = step_size,)
optim = tf.optimizers.Adam(params['init_lr'])
global_step = 0

best_acc = .0
count = 0

t0 = time.time()
logger = logging.getLogger('tensorflow')
logger.setLevel(logging.INFO)

while True:
  # TRAINING
  for (text, seg, labels) in dataset(is_training=True, params=params):
    with tf.GradientTape() as tape:
      logits = model([text, tf.sign(text), seg], training=True)
      loss = tf.compat.v1.losses.softmax_cross_entropy(
        tf.one_hot(labels, 3, dtype=tf.float32),
        logits = logits,
        label_smoothing = .2,)
      
    optim.lr.assign(decay_lr(global_step))
    grads = tape.gradient(loss, model.trainable_variables)
    grads, _ = tf.clip_by_global_norm(grads, 5.)
    optim.apply_gradients(zip(grads, model.trainable_variables))
    
    if global_step % 100 == 0:
      logger.info("Step {} | Loss: {:.4f} | Spent: {:.1f} secs | LR: {:.6f}".format(
          global_step, loss.numpy().item(), time.time()-t0, optim.lr.numpy().item()))
      t0 = time.time()
    global_step += 1
  
  # EVALUATION
  m = tf.keras.metrics.Accuracy()

  for (text, seg, labels) in dataset(is_training=False, params=params):
    logits = model([text, tf.sign(text), seg], training=False)
    m.update_state(y_true=labels, y_pred=tf.argmax(logits, -1))

  acc = m.result().numpy()
  logger.info("Evaluation: Testing Accuracy: {:.3f}".format(acc))

  if acc > best_acc:
    best_acc = acc
    # you can save model here
    count = 0
  else:
    count += 1
  logger.info("Best Accuracy: {:.3f}".format(best_acc))

  if count == params['num_patience']:
    print(params['num_patience'], "times not improve the best result, therefore stop training")
    break
Some weights of the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['nsp___cls', 'mlm___cls']
- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).
- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
All the weights of TFBertModel were initialized from the model checkpoint at bert-base-uncased.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use TFBertModel for predictions without further training.
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Reading ../data/train.txt
INFO:tensorflow:Step 0 | Loss: 1.8435 | Spent: 110.6 secs | LR: 0.000010
INFO:tensorflow:Step 100 | Loss: 1.0236 | Spent: 36.7 secs | LR: 0.000010
INFO:tensorflow:Step 200 | Loss: 0.8444 | Spent: 35.9 secs | LR: 0.000010
INFO:tensorflow:Step 300 | Loss: 0.8050 | Spent: 35.8 secs | LR: 0.000010
INFO:tensorflow:Step 400 | Loss: 0.8626 | Spent: 35.8 secs | LR: 0.000010
INFO:tensorflow:Step 500 | Loss: 0.9565 | Spent: 36.1 secs | LR: 0.000010
INFO:tensorflow:Step 600 | Loss: 0.7018 | Spent: 35.5 secs | LR: 0.000010
INFO:tensorflow:Step 700 | Loss: 0.7958 | Spent: 35.9 secs | LR: 0.000010
INFO:tensorflow:Step 800 | Loss: 0.8965 | Spent: 36.0 secs | LR: 0.000010
INFO:tensorflow:Step 900 | Loss: 0.8253 | Spent: 36.1 secs | LR: 0.000011
INFO:tensorflow:Step 1000 | Loss: 0.6568 | Spent: 36.2 secs | LR: 0.000011
INFO:tensorflow:Step 1100 | Loss: 0.7459 | Spent: 36.1 secs | LR: 0.000011
INFO:tensorflow:Step 1200 | Loss: 0.8463 | Spent: 35.8 secs | LR: 0.000011
INFO:tensorflow:Step 1300 | Loss: 0.7047 | Spent: 36.2 secs | LR: 0.000011
INFO:tensorflow:Step 1400 | Loss: 0.7500 | Spent: 36.2 secs | LR: 0.000011
INFO:tensorflow:Step 1500 | Loss: 0.6935 | Spent: 36.0 secs | LR: 0.000011
INFO:tensorflow:Step 1600 | Loss: 0.8273 | Spent: 36.1 secs | LR: 0.000011
INFO:tensorflow:Step 1700 | Loss: 0.8394 | Spent: 36.3 secs | LR: 0.000011
INFO:tensorflow:Step 1800 | Loss: 0.7715 | Spent: 36.6 secs | LR: 0.000011
INFO:tensorflow:Step 1900 | Loss: 0.7142 | Spent: 35.9 secs | LR: 0.000011
INFO:tensorflow:Step 2000 | Loss: 0.7235 | Spent: 35.9 secs | LR: 0.000011
INFO:tensorflow:Step 2100 | Loss: 0.7053 | Spent: 36.0 secs | LR: 0.000011
INFO:tensorflow:Step 2200 | Loss: 0.8156 | Spent: 35.9 secs | LR: 0.000011
INFO:tensorflow:Step 2300 | Loss: 0.5950 | Spent: 35.6 secs | LR: 0.000011
INFO:tensorflow:Step 2400 | Loss: 0.7681 | Spent: 36.1 secs | LR: 0.000011
INFO:tensorflow:Step 2500 | Loss: 0.7495 | Spent: 36.6 secs | LR: 0.000011
INFO:tensorflow:Step 2600 | Loss: 0.7219 | Spent: 36.1 secs | LR: 0.000012
INFO:tensorflow:Step 2700 | Loss: 0.6558 | Spent: 36.2 secs | LR: 0.000012
INFO:tensorflow:Step 2800 | Loss: 0.7030 | Spent: 36.1 secs | LR: 0.000012
INFO:tensorflow:Step 2900 | Loss: 0.6590 | Spent: 36.1 secs | LR: 0.000012
INFO:tensorflow:Step 3000 | Loss: 0.6591 | Spent: 36.4 secs | LR: 0.000012
INFO:tensorflow:Step 3100 | Loss: 0.6524 | Spent: 36.3 secs | LR: 0.000012
INFO:tensorflow:Step 3200 | Loss: 0.7025 | Spent: 36.3 secs | LR: 0.000012
INFO:tensorflow:Step 3300 | Loss: 0.7265 | Spent: 36.8 secs | LR: 0.000012
INFO:tensorflow:Step 3400 | Loss: 0.6177 | Spent: 36.5 secs | LR: 0.000012
INFO:tensorflow:Step 3500 | Loss: 0.7065 | Spent: 37.0 secs | LR: 0.000012
INFO:tensorflow:Step 3600 | Loss: 0.7193 | Spent: 36.5 secs | LR: 0.000012
INFO:tensorflow:Step 3700 | Loss: 0.6631 | Spent: 36.7 secs | LR: 0.000012
INFO:tensorflow:Step 3800 | Loss: 0.6811 | Spent: 37.4 secs | LR: 0.000012
INFO:tensorflow:Step 3900 | Loss: 0.6251 | Spent: 36.9 secs | LR: 0.000012
INFO:tensorflow:Step 4000 | Loss: 0.5522 | Spent: 36.5 secs | LR: 0.000012
INFO:tensorflow:Step 4100 | Loss: 0.7566 | Spent: 36.4 secs | LR: 0.000012
INFO:tensorflow:Step 4200 | Loss: 0.7144 | Spent: 36.4 secs | LR: 0.000012
INFO:tensorflow:Step 4300 | Loss: 0.9102 | Spent: 36.9 secs | LR: 0.000013
INFO:tensorflow:Step 4400 | Loss: 0.5101 | Spent: 37.1 secs | LR: 0.000013
INFO:tensorflow:Step 4500 | Loss: 0.7490 | Spent: 36.2 secs | LR: 0.000013
INFO:tensorflow:Step 4600 | Loss: 0.7715 | Spent: 36.5 secs | LR: 0.000013
INFO:tensorflow:Step 4700 | Loss: 0.6188 | Spent: 36.1 secs | LR: 0.000013
INFO:tensorflow:Step 4800 | Loss: 0.7160 | Spent: 36.3 secs | LR: 0.000013
INFO:tensorflow:Step 4900 | Loss: 0.6564 | Spent: 36.6 secs | LR: 0.000013
INFO:tensorflow:Step 5000 | Loss: 0.6663 | Spent: 36.2 secs | LR: 0.000013
INFO:tensorflow:Step 5100 | Loss: 0.6541 | Spent: 36.1 secs | LR: 0.000013
INFO:tensorflow:Step 5200 | Loss: 0.6436 | Spent: 37.2 secs | LR: 0.000013
INFO:tensorflow:Step 5300 | Loss: 0.7380 | Spent: 36.5 secs | LR: 0.000013
INFO:tensorflow:Step 5400 | Loss: 0.7450 | Spent: 36.3 secs | LR: 0.000013
INFO:tensorflow:Step 5500 | Loss: 0.8775 | Spent: 36.4 secs | LR: 0.000013
INFO:tensorflow:Step 5600 | Loss: 0.6815 | Spent: 35.9 secs | LR: 0.000013
INFO:tensorflow:Step 5700 | Loss: 0.7071 | Spent: 36.3 secs | LR: 0.000013
INFO:tensorflow:Step 5800 | Loss: 0.7261 | Spent: 36.0 secs | LR: 0.000013
INFO:tensorflow:Step 5900 | Loss: 0.7193 | Spent: 35.9 secs | LR: 0.000013
INFO:tensorflow:Step 6000 | Loss: 0.7431 | Spent: 35.8 secs | LR: 0.000013
INFO:tensorflow:Step 6100 | Loss: 0.6846 | Spent: 36.5 secs | LR: 0.000014
INFO:tensorflow:Step 6200 | Loss: 0.6446 | Spent: 36.4 secs | LR: 0.000014
INFO:tensorflow:Step 6300 | Loss: 0.7656 | Spent: 36.1 secs | LR: 0.000014
INFO:tensorflow:Step 6400 | Loss: 0.7752 | Spent: 36.0 secs | LR: 0.000014
INFO:tensorflow:Step 6500 | Loss: 0.7117 | Spent: 36.4 secs | LR: 0.000014
INFO:tensorflow:Step 6600 | Loss: 0.6401 | Spent: 36.2 secs | LR: 0.000014
INFO:tensorflow:Step 6700 | Loss: 0.6603 | Spent: 36.4 secs | LR: 0.000014
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INFO:tensorflow:Step 7000 | Loss: 0.7477 | Spent: 36.8 secs | LR: 0.000014
INFO:tensorflow:Step 7100 | Loss: 0.6598 | Spent: 36.9 secs | LR: 0.000014
INFO:tensorflow:Step 7200 | Loss: 0.6491 | Spent: 36.9 secs | LR: 0.000014
INFO:tensorflow:Step 7300 | Loss: 0.6803 | Spent: 36.7 secs | LR: 0.000014
INFO:tensorflow:Step 7400 | Loss: 0.8075 | Spent: 36.5 secs | LR: 0.000014
INFO:tensorflow:Step 7500 | Loss: 0.7558 | Spent: 36.5 secs | LR: 0.000014
INFO:tensorflow:Step 7600 | Loss: 0.6588 | Spent: 36.9 secs | LR: 0.000014
INFO:tensorflow:Step 7700 | Loss: 0.6836 | Spent: 36.7 secs | LR: 0.000014
INFO:tensorflow:Step 7800 | Loss: 0.5938 | Spent: 37.2 secs | LR: 0.000015
INFO:tensorflow:Step 7900 | Loss: 0.5817 | Spent: 37.0 secs | LR: 0.000015
INFO:tensorflow:Step 8000 | Loss: 0.7613 | Spent: 36.7 secs | LR: 0.000015
INFO:tensorflow:Step 8100 | Loss: 0.6526 | Spent: 37.0 secs | LR: 0.000015
INFO:tensorflow:Step 8200 | Loss: 0.6828 | Spent: 36.8 secs | LR: 0.000015
INFO:tensorflow:Step 8300 | Loss: 0.7024 | Spent: 36.7 secs | LR: 0.000015
INFO:tensorflow:Step 8400 | Loss: 0.6794 | Spent: 37.1 secs | LR: 0.000015
INFO:tensorflow:Step 8500 | Loss: 0.6328 | Spent: 37.4 secs | LR: 0.000015
INFO:tensorflow:Step 8600 | Loss: 0.6815 | Spent: 37.5 secs | LR: 0.000015
INFO:tensorflow:Step 8700 | Loss: 0.6209 | Spent: 37.1 secs | LR: 0.000015
INFO:tensorflow:Step 8800 | Loss: 0.5675 | Spent: 37.0 secs | LR: 0.000015
INFO:tensorflow:Step 8900 | Loss: 0.6632 | Spent: 36.8 secs | LR: 0.000015
INFO:tensorflow:Step 9000 | Loss: 0.7650 | Spent: 37.3 secs | LR: 0.000015
INFO:tensorflow:Step 9100 | Loss: 0.8982 | Spent: 37.1 secs | LR: 0.000015
INFO:tensorflow:Step 9200 | Loss: 0.6037 | Spent: 37.1 secs | LR: 0.000015
INFO:tensorflow:Step 9300 | Loss: 0.7583 | Spent: 36.8 secs | LR: 0.000015
INFO:tensorflow:Step 9400 | Loss: 0.7273 | Spent: 37.2 secs | LR: 0.000015
INFO:tensorflow:Step 9500 | Loss: 0.6352 | Spent: 37.0 secs | LR: 0.000016
INFO:tensorflow:Step 9600 | Loss: 0.6947 | Spent: 37.0 secs | LR: 0.000016
INFO:tensorflow:Step 9700 | Loss: 0.6001 | Spent: 36.8 secs | LR: 0.000016
INFO:tensorflow:Step 9800 | Loss: 0.6025 | Spent: 37.2 secs | LR: 0.000016
INFO:tensorflow:Step 9900 | Loss: 0.7405 | Spent: 37.2 secs | LR: 0.000016
INFO:tensorflow:Step 10000 | Loss: 0.6559 | Spent: 37.1 secs | LR: 0.000016
INFO:tensorflow:Step 10100 | Loss: 0.5362 | Spent: 37.3 secs | LR: 0.000016
INFO:tensorflow:Step 10200 | Loss: 0.6322 | Spent: 37.7 secs | LR: 0.000016
INFO:tensorflow:Step 10300 | Loss: 0.6483 | Spent: 38.1 secs | LR: 0.000016
INFO:tensorflow:Step 10400 | Loss: 0.6942 | Spent: 37.7 secs | LR: 0.000016
INFO:tensorflow:Step 10500 | Loss: 0.7047 | Spent: 37.6 secs | LR: 0.000016
INFO:tensorflow:Step 10600 | Loss: 0.6971 | Spent: 37.3 secs | LR: 0.000016
INFO:tensorflow:Step 10700 | Loss: 0.6579 | Spent: 37.3 secs | LR: 0.000016
INFO:tensorflow:Step 10800 | Loss: 0.8495 | Spent: 37.1 secs | LR: 0.000016
INFO:tensorflow:Step 10900 | Loss: 0.7805 | Spent: 37.6 secs | LR: 0.000016
INFO:tensorflow:Step 11000 | Loss: 0.5842 | Spent: 35.9 secs | LR: 0.000016
INFO:tensorflow:Step 11100 | Loss: 0.6624 | Spent: 35.6 secs | LR: 0.000016
INFO:tensorflow:Step 11200 | Loss: 0.6767 | Spent: 35.1 secs | LR: 0.000017
INFO:tensorflow:Step 11300 | Loss: 0.6285 | Spent: 35.5 secs | LR: 0.000017
INFO:tensorflow:Step 11400 | Loss: 0.6203 | Spent: 35.5 secs | LR: 0.000017
INFO:tensorflow:Step 11500 | Loss: 0.6680 | Spent: 35.3 secs | LR: 0.000017
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INFO:tensorflow:Step 11800 | Loss: 0.7180 | Spent: 35.7 secs | LR: 0.000017
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INFO:tensorflow:Step 12000 | Loss: 0.7328 | Spent: 35.4 secs | LR: 0.000017
INFO:tensorflow:Step 12100 | Loss: 0.6072 | Spent: 34.8 secs | LR: 0.000017
INFO:tensorflow:Step 12200 | Loss: 0.6803 | Spent: 34.6 secs | LR: 0.000017
INFO:tensorflow:Step 12300 | Loss: 0.6744 | Spent: 34.8 secs | LR: 0.000017
INFO:tensorflow:Step 12400 | Loss: 0.6587 | Spent: 34.5 secs | LR: 0.000017
INFO:tensorflow:Step 12500 | Loss: 0.7730 | Spent: 34.5 secs | LR: 0.000017
INFO:tensorflow:Step 12600 | Loss: 0.5997 | Spent: 34.7 secs | LR: 0.000017
INFO:tensorflow:Step 12700 | Loss: 0.7673 | Spent: 35.0 secs | LR: 0.000017
INFO:tensorflow:Step 12800 | Loss: 0.7345 | Spent: 34.7 secs | LR: 0.000017
INFO:tensorflow:Step 12900 | Loss: 0.6859 | Spent: 34.7 secs | LR: 0.000018
INFO:tensorflow:Step 13000 | Loss: 0.6579 | Spent: 35.0 secs | LR: 0.000018
INFO:tensorflow:Step 13100 | Loss: 0.6804 | Spent: 34.4 secs | LR: 0.000018
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INFO:tensorflow:Step 16000 | Loss: 0.7553 | Spent: 34.4 secs | LR: 0.000019
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.896
INFO:tensorflow:Best Accuracy: 0.896
Reading ../data/train.txt
INFO:tensorflow:Step 17200 | Loss: 0.7541 | Spent: 186.2 secs | LR: 0.000020
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.900
INFO:tensorflow:Best Accuracy: 0.900
Reading ../data/train.txt
INFO:tensorflow:Step 34400 | Loss: 0.5718 | Spent: 185.9 secs | LR: 0.000030
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.902
INFO:tensorflow:Best Accuracy: 0.902
Reading ../data/train.txt
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INFO:tensorflow:Step 64800 | Loss: 0.5550 | Spent: 34.1 secs | LR: 0.000012
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INFO:tensorflow:Step 65000 | Loss: 0.5434 | Spent: 33.8 secs | LR: 0.000012
INFO:tensorflow:Step 65100 | Loss: 0.5539 | Spent: 34.2 secs | LR: 0.000012
INFO:tensorflow:Step 65200 | Loss: 0.6266 | Spent: 34.2 secs | LR: 0.000012
INFO:tensorflow:Step 65300 | Loss: 0.5874 | Spent: 34.1 secs | LR: 0.000012
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INFO:tensorflow:Step 65800 | Loss: 0.5818 | Spent: 34.0 secs | LR: 0.000012
INFO:tensorflow:Step 65900 | Loss: 0.6009 | Spent: 34.0 secs | LR: 0.000012
INFO:tensorflow:Step 66000 | Loss: 0.6109 | Spent: 34.2 secs | LR: 0.000012
INFO:tensorflow:Step 66100 | Loss: 0.6376 | Spent: 33.9 secs | LR: 0.000012
INFO:tensorflow:Step 66200 | Loss: 0.5192 | Spent: 34.2 secs | LR: 0.000011
INFO:tensorflow:Step 66300 | Loss: 0.5673 | Spent: 33.9 secs | LR: 0.000011
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INFO:tensorflow:Step 66500 | Loss: 0.5311 | Spent: 34.3 secs | LR: 0.000011
INFO:tensorflow:Step 66600 | Loss: 0.6332 | Spent: 34.1 secs | LR: 0.000011
INFO:tensorflow:Step 66700 | Loss: 0.5426 | Spent: 34.2 secs | LR: 0.000011
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INFO:tensorflow:Step 66900 | Loss: 0.6124 | Spent: 34.3 secs | LR: 0.000011
INFO:tensorflow:Step 67000 | Loss: 0.5464 | Spent: 34.4 secs | LR: 0.000011
INFO:tensorflow:Step 67100 | Loss: 0.6132 | Spent: 34.0 secs | LR: 0.000011
INFO:tensorflow:Step 67200 | Loss: 0.5092 | Spent: 34.4 secs | LR: 0.000011
INFO:tensorflow:Step 67300 | Loss: 0.6827 | Spent: 34.3 secs | LR: 0.000011
INFO:tensorflow:Step 67400 | Loss: 0.5661 | Spent: 34.6 secs | LR: 0.000011
INFO:tensorflow:Step 67500 | Loss: 0.5917 | Spent: 34.1 secs | LR: 0.000011
INFO:tensorflow:Step 67600 | Loss: 0.6259 | Spent: 34.3 secs | LR: 0.000011
INFO:tensorflow:Step 67700 | Loss: 0.5517 | Spent: 34.3 secs | LR: 0.000011
INFO:tensorflow:Step 67800 | Loss: 0.5539 | Spent: 34.3 secs | LR: 0.000011
INFO:tensorflow:Step 67900 | Loss: 0.5697 | Spent: 34.5 secs | LR: 0.000011
INFO:tensorflow:Step 68000 | Loss: 0.5514 | Spent: 34.1 secs | LR: 0.000010
INFO:tensorflow:Step 68100 | Loss: 0.5657 | Spent: 34.2 secs | LR: 0.000010
INFO:tensorflow:Step 68200 | Loss: 0.5579 | Spent: 34.2 secs | LR: 0.000010
INFO:tensorflow:Step 68300 | Loss: 0.5520 | Spent: 34.1 secs | LR: 0.000010
INFO:tensorflow:Step 68400 | Loss: 0.5526 | Spent: 34.6 secs | LR: 0.000010
INFO:tensorflow:Step 68500 | Loss: 0.5480 | Spent: 34.3 secs | LR: 0.000010
INFO:tensorflow:Step 68600 | Loss: 0.5647 | Spent: 34.0 secs | LR: 0.000010
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.904
INFO:tensorflow:Best Accuracy: 0.904
Reading ../data/train.txt