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
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)
In [3]:
import tensorflow as tf
import numpy as np
import pprint
import logging
import time
import math


from tensorflow_addons.optimizers.cyclical_learning_rate import Triangular2CyclicalLearningRate

print("TensorFlow Version", tf.__version__)
print('GPU Enabled:', tf.test.is_gpu_available())
TensorFlow Version 2.2.0
WARNING:tensorflow:From <ipython-input-3-62847ee0f62b>:12: 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 [0]:
def get_vocab(f_path):
  word2idx = {}
  with open(f_path) as f:
    for i, line in enumerate(f):
      line = line.rstrip()
      word2idx[line] = i
  return word2idx
In [0]:
# stream data from text files
def data_generator(f_path, word2idx):
  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 = [word2idx.get(w, len(word2idx)) for w in text1.split()]
      text2 = [word2idx.get(w, len(word2idx)) for w in text2.split()]
      yield ((text1, text2), 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['word2idx']),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.shuffle(params['buffer_size'])
    ds = ds.padded_batch(params['batch_size'], _shapes, _pads)
  
  else:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['test_path'], params['word2idx']),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.padded_batch(params['batch_size'], _shapes, _pads)
  
  return ds
In [0]:
class RE2(tf.keras.Model):
  def __init__(self, params: dict):
    super().__init__()
    
    self.pretrained_embedding = tf.Variable(np.load(params['embedding_path']), name='pretrained_embedding')
    self.embed_dropout = tf.keras.layers.Dropout(params['dropout_rate'])
    
    self.birnn = [tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
        params['hidden_size'], return_sequences=True), name='birnn_%d'%(i+1)) for i in range(params['num_blocks'])]
    self.enc_dropout = tf.keras.layers.Dropout(params['dropout_rate'])
    
    self.align_t = self.add_weight(name='temperature',
                                   shape=(),
                                   trainable=True,
                                   initializer=tf.initializers.constant(math.sqrt(1/params['hidden_size'])))
    self.align_dropout = tf.keras.layers.Dropout(params['dropout_rate'])
    self.align_fc1 = [tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='align_fc1_%d'%(i+1)) for i in range(params['num_blocks'])]
    self.align_fc2 = [tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='align_fc2_%d'%(i+1)) for i in range(params['num_blocks'])]
      
    self.fusion_fc1 = [tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='fusion_fc1_%d'%(i+1)) for i in range(params['num_blocks'])]
    self.fusion_fc2 = [tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='fusion_fc2_%d'%(i+1)) for i in range(params['num_blocks'])]
    self.fusion_fc3 = [tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='fusion_fc3_%d'%(i+1)) for i in range(params['num_blocks'])]
    self.fusion_fc4 = [tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='fusion_fc4_%d'%(i+1)) for i in range(params['num_blocks'])]
    self.fusion_dropout = tf.keras.layers.Dropout(params['dropout_rate'])
    
    self.out_drop1 = tf.keras.layers.Dropout(params['dropout_rate'])
    self.out_fc = tf.keras.layers.Dense(params['hidden_size'], params['activation'], name='out_fc')
    self.out_drop2 = tf.keras.layers.Dropout(params['dropout_rate'])
    self.out_linear = tf.keras.layers.Dense(params['num_labels'], name='out_linear')
    
  
  
  def call(self, inputs, training=False):
    x1, x2 = inputs
    
    if x1.dtype != tf.int32:
      x1 = tf.cast(x1, tf.int32)
    if x2.dtype != tf.int32:
      x2 = tf.cast(x2, tf.int32)
    
    batch_sz = tf.shape(x1)[0]
    
    mask1 = tf.sign(x1)
    mask2 = tf.sign(x2)
    
    x1 = self.embedding(x1, training=training)
    x2 = self.embedding(x2, training=training)
    
    res_x1, res_x2 = x1, x2
    
    for i in range(params['num_blocks']):
      
      if i > 0:
        x1 = self.connection(x1, res_x1, i)
        x2 = self.connection(x2, res_x2, i)
        res_x1, res_x2 = x1, x2
    
      x1_enc = self.encoding(x1, mask1, i, training=training)
      x2_enc = self.encoding(x2, mask2, i, training=training)
    
      x1 = tf.concat((x1, x1_enc), -1)
      x2 = tf.concat((x2, x2_enc), -1)
      
      align_1, align_2 = self.alignment(x1, x2, mask1, mask2, i, training=training)
      
      x1 = self.fusion(x1, align_1, i, training=training)
      x2 = self.fusion(x2, align_2, i, training=training)
    
    x1 = self.pooling(x1, mask1)
    x2 = self.pooling(x2, mask2)
    
    x = self.prediction(x1, x2, training=training)
    
    return x
  
  
  def embedding(self, x, training):
    x = tf.nn.embedding_lookup(self.pretrained_embedding, x)
    x = self.embed_dropout(x, training=training)
    return x
  
  
  def connection(self, x, res, i):
    if i == 1:
      x = tf.concat((res, x), -1)
    elif i > 1:
      hidden_size = x.shape[-1]
      x = (res[:, :, -hidden_size:] + x) * tf.math.sqrt(0.5)
      x = tf.concat((res[:, :, :-hidden_size], x), -1)
    return x
    
    
  def encoding(self, x, mask, i, training):
    mask = tf.cast(tf.expand_dims(mask, -1), tf.float32)
    x *= mask
    x = self.birnn[i](x)
    x = self.enc_dropout(x, training=training)
    return x
  
  
  def alignment(self, x1, x2, mask1, mask2, i, training):
    mask1 = tf.cast(tf.expand_dims(mask1, -1), tf.float32)
    mask2 = tf.cast(tf.expand_dims(mask2, -1), tf.float32)
    
    x1_ = self.align_fc1[i](self.align_dropout(x1, training=training))
    x2_ = self.align_fc2[i](self.align_dropout(x2, training=training))
    align = tf.matmul(x1_, x2_, transpose_b=True) * self.align_t
    mask = tf.matmul(mask1, mask2, transpose_b=True)
    align = mask * align + (1 - mask) * tf.float32.min
    align_1 = tf.nn.softmax(align, 1)
    align_2 = tf.nn.softmax(align, 2)
    
    x2 = tf.matmul(align_1, x1, transpose_a=True)
    x1 = tf.matmul(align_2, x2)
    return x1, x2
  
  
  def fusion(self, x, align, i, training):
    x = tf.concat([self.fusion_fc1[i](tf.concat((x, align), -1)),
                   self.fusion_fc2[i](tf.concat((x, x - align), -1)),
                   self.fusion_fc3[i](tf.concat((x, x * align), -1))], -1)
    x = self.fusion_dropout(x, training=training)
    x = self.fusion_fc4[i](x)
    return x
  
  
  def pooling(self, x, mask):
    mask = tf.cast(tf.expand_dims(mask, -1), tf.float32)
    return tf.reduce_max(x * mask, 1)
  
  
  def prediction(self, x1, x2, training):
    x = tf.concat((x1, x2, x1 * x2, x1 - x2), -1)
    x = self.out_drop1(x, training=training)
    x = self.out_fc(x)
    x = self.out_drop2(x, training=training)
    x = self.out_linear(x)
    return x
In [0]:
params = {
    'model_dir': '../model/re2_birnn',
    'log_path': '../log/re2_birnn.txt',
    'train_path': '../data/train.txt',
    'test_path': '../data/test.txt',
    'embedding_path': '../vocab/word.npy',
    'vocab_path': '../vocab/word.txt',
    'batch_size': 256,
    'buffer_size': 200000,
    'num_blocks': 3,
    'dropout_rate': .2,
    'hidden_size': 150,
    'activation': tf.nn.relu,
    'init_lr': 1e-4,
    'max_lr': 8e-4,
    'clip_norm': 5.,
    'num_labels': 3,
    'num_patience': 10,
}
In [8]:
params['word2idx'] = get_vocab(params['vocab_path'])
params['vocab_size'] = len(params['word2idx']) + 1

model = RE2(params)
model.build([[None, None], [None, None]])
pprint.pprint([(v.name, v.shape) for v in model.trainable_variables])

decay_lr = Triangular2CyclicalLearningRate(
  initial_learning_rate = params['init_lr'],
  maximal_learning_rate = params['max_lr'],
  step_size = 4 * 550152 // params['batch_size'],)
optim = tf.optimizers.Adam(params['init_lr'])
global_step = 0

count = 0
best_acc = .0

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

while True:
  # TRAINING
  for ((text1, text2), label) in dataset(is_training=True, params=params):
    with tf.GradientTape() as tape:
      logits = model((text1, text2), training=True)
      loss = tf.losses.categorical_crossentropy(y_true = tf.one_hot(label, params['num_labels']),
                                                y_pred = logits,
                                                from_logits = True,
                                                label_smoothing = .2,)
      loss = tf.reduce_mean(loss)
      
    optim.lr.assign(decay_lr(global_step))
    grads = tape.gradient(loss, model.trainable_variables)
    grads, _ = tf.clip_by_global_norm(grads, params['clip_norm'])
    optim.apply_gradients(zip(grads, model.trainable_variables))
    
    if global_step % 50 == 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 ((text1, text2), label) in dataset(is_training=False, params=params):
    logits = model((text1, text2), training=False)
    y_pred = tf.argmax(logits, axis=-1)
    m.update_state(y_true=label, y_pred=y_pred)

  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
[('birnn_1/forward_lstm/lstm_cell_1/kernel:0', TensorShape([300, 600])),
 ('birnn_1/forward_lstm/lstm_cell_1/recurrent_kernel:0',
  TensorShape([150, 600])),
 ('birnn_1/forward_lstm/lstm_cell_1/bias:0', TensorShape([600])),
 ('birnn_1/backward_lstm/lstm_cell_2/kernel:0', TensorShape([300, 600])),
 ('birnn_1/backward_lstm/lstm_cell_2/recurrent_kernel:0',
  TensorShape([150, 600])),
 ('birnn_1/backward_lstm/lstm_cell_2/bias:0', TensorShape([600])),
 ('birnn_2/forward_lstm_1/lstm_cell_4/kernel:0', TensorShape([450, 600])),
 ('birnn_2/forward_lstm_1/lstm_cell_4/recurrent_kernel:0',
  TensorShape([150, 600])),
 ('birnn_2/forward_lstm_1/lstm_cell_4/bias:0', TensorShape([600])),
 ('birnn_2/backward_lstm_1/lstm_cell_5/kernel:0', TensorShape([450, 600])),
 ('birnn_2/backward_lstm_1/lstm_cell_5/recurrent_kernel:0',
  TensorShape([150, 600])),
 ('birnn_2/backward_lstm_1/lstm_cell_5/bias:0', TensorShape([600])),
 ('birnn_3/forward_lstm_2/lstm_cell_7/kernel:0', TensorShape([450, 600])),
 ('birnn_3/forward_lstm_2/lstm_cell_7/recurrent_kernel:0',
  TensorShape([150, 600])),
 ('birnn_3/forward_lstm_2/lstm_cell_7/bias:0', TensorShape([600])),
 ('birnn_3/backward_lstm_2/lstm_cell_8/kernel:0', TensorShape([450, 600])),
 ('birnn_3/backward_lstm_2/lstm_cell_8/recurrent_kernel:0',
  TensorShape([150, 600])),
 ('birnn_3/backward_lstm_2/lstm_cell_8/bias:0', TensorShape([600])),
 ('align_fc1_1/kernel:0', TensorShape([600, 150])),
 ('align_fc1_1/bias:0', TensorShape([150])),
 ('align_fc1_2/kernel:0', TensorShape([750, 150])),
 ('align_fc1_2/bias:0', TensorShape([150])),
 ('align_fc1_3/kernel:0', TensorShape([750, 150])),
 ('align_fc1_3/bias:0', TensorShape([150])),
 ('align_fc2_1/kernel:0', TensorShape([600, 150])),
 ('align_fc2_1/bias:0', TensorShape([150])),
 ('align_fc2_2/kernel:0', TensorShape([750, 150])),
 ('align_fc2_2/bias:0', TensorShape([150])),
 ('align_fc2_3/kernel:0', TensorShape([750, 150])),
 ('align_fc2_3/bias:0', TensorShape([150])),
 ('fusion_fc1_1/kernel:0', TensorShape([1200, 150])),
 ('fusion_fc1_1/bias:0', TensorShape([150])),
 ('fusion_fc1_2/kernel:0', TensorShape([1500, 150])),
 ('fusion_fc1_2/bias:0', TensorShape([150])),
 ('fusion_fc1_3/kernel:0', TensorShape([1500, 150])),
 ('fusion_fc1_3/bias:0', TensorShape([150])),
 ('fusion_fc2_1/kernel:0', TensorShape([1200, 150])),
 ('fusion_fc2_1/bias:0', TensorShape([150])),
 ('fusion_fc2_2/kernel:0', TensorShape([1500, 150])),
 ('fusion_fc2_2/bias:0', TensorShape([150])),
 ('fusion_fc2_3/kernel:0', TensorShape([1500, 150])),
 ('fusion_fc2_3/bias:0', TensorShape([150])),
 ('fusion_fc3_1/kernel:0', TensorShape([1200, 150])),
 ('fusion_fc3_1/bias:0', TensorShape([150])),
 ('fusion_fc3_2/kernel:0', TensorShape([1500, 150])),
 ('fusion_fc3_2/bias:0', TensorShape([150])),
 ('fusion_fc3_3/kernel:0', TensorShape([1500, 150])),
 ('fusion_fc3_3/bias:0', TensorShape([150])),
 ('fusion_fc4_1/kernel:0', TensorShape([450, 150])),
 ('fusion_fc4_1/bias:0', TensorShape([150])),
 ('fusion_fc4_2/kernel:0', TensorShape([450, 150])),
 ('fusion_fc4_2/bias:0', TensorShape([150])),
 ('fusion_fc4_3/kernel:0', TensorShape([450, 150])),
 ('fusion_fc4_3/bias:0', TensorShape([150])),
 ('out_fc/kernel:0', TensorShape([600, 150])),
 ('out_fc/bias:0', TensorShape([150])),
 ('out_linear/kernel:0', TensorShape([150, 3])),
 ('out_linear/bias:0', TensorShape([3])),
 ('pretrained_embedding:0', TensorShape([20884, 300])),
 ('temperature:0', TensorShape([]))]
Reading ../data/train.txt
INFO:tensorflow:Step 0 | Loss: 1.1418 | Spent: 36.3 secs | LR: 0.000100
INFO:tensorflow:Step 50 | Loss: 1.0915 | Spent: 12.8 secs | LR: 0.000104
INFO:tensorflow:Step 100 | Loss: 1.0744 | Spent: 12.9 secs | LR: 0.000108
INFO:tensorflow:Step 150 | Loss: 1.0060 | Spent: 12.8 secs | LR: 0.000112
INFO:tensorflow:Step 200 | Loss: 1.0167 | Spent: 12.4 secs | LR: 0.000116
INFO:tensorflow:Step 250 | Loss: 0.9999 | Spent: 12.8 secs | LR: 0.000120
INFO:tensorflow:Step 300 | Loss: 0.9448 | Spent: 12.9 secs | LR: 0.000124
INFO:tensorflow:Step 350 | Loss: 0.9428 | Spent: 12.8 secs | LR: 0.000129
INFO:tensorflow:Step 400 | Loss: 0.9289 | Spent: 12.8 secs | LR: 0.000133
INFO:tensorflow:Step 450 | Loss: 0.9081 | Spent: 12.5 secs | LR: 0.000137
INFO:tensorflow:Step 500 | Loss: 0.8703 | Spent: 12.7 secs | LR: 0.000141
INFO:tensorflow:Step 550 | Loss: 0.8715 | Spent: 13.0 secs | LR: 0.000145
INFO:tensorflow:Step 600 | Loss: 0.8930 | Spent: 12.6 secs | LR: 0.000149
INFO:tensorflow:Step 650 | Loss: 0.8632 | Spent: 12.6 secs | LR: 0.000153
INFO:tensorflow:Step 700 | Loss: 0.8534 | Spent: 12.6 secs | LR: 0.000157
INFO:tensorflow:Step 750 | Loss: 0.8594 | Spent: 13.0 secs | LR: 0.000161
INFO:tensorflow:Step 800 | Loss: 0.8699 | Spent: 12.5 secs | LR: 0.000165
INFO:tensorflow:Step 850 | Loss: 0.8327 | Spent: 12.5 secs | LR: 0.000169
INFO:tensorflow:Step 900 | Loss: 0.8103 | Spent: 12.5 secs | LR: 0.000173
INFO:tensorflow:Step 950 | Loss: 0.8490 | Spent: 12.8 secs | LR: 0.000177
INFO:tensorflow:Step 1000 | Loss: 0.8068 | Spent: 12.5 secs | LR: 0.000181
INFO:tensorflow:Step 1050 | Loss: 0.8271 | Spent: 12.6 secs | LR: 0.000186
INFO:tensorflow:Step 1100 | Loss: 0.8026 | Spent: 13.0 secs | LR: 0.000190
INFO:tensorflow:Step 1150 | Loss: 0.7902 | Spent: 12.5 secs | LR: 0.000194
INFO:tensorflow:Step 1200 | Loss: 0.8349 | Spent: 12.2 secs | LR: 0.000198
INFO:tensorflow:Step 1250 | Loss: 0.7714 | Spent: 12.6 secs | LR: 0.000202
INFO:tensorflow:Step 1300 | Loss: 0.7959 | Spent: 12.3 secs | LR: 0.000206
INFO:tensorflow:Step 1350 | Loss: 0.8145 | Spent: 13.0 secs | LR: 0.000210
INFO:tensorflow:Step 1400 | Loss: 0.8149 | Spent: 11.1 secs | LR: 0.000214
INFO:tensorflow:Step 1450 | Loss: 0.7969 | Spent: 10.5 secs | LR: 0.000218
INFO:tensorflow:Step 1500 | Loss: 0.8212 | Spent: 10.7 secs | LR: 0.000222
INFO:tensorflow:Step 1550 | Loss: 0.8027 | Spent: 10.6 secs | LR: 0.000226
INFO:tensorflow:Step 1600 | Loss: 0.8065 | Spent: 10.6 secs | LR: 0.000230
INFO:tensorflow:Step 1650 | Loss: 0.8188 | Spent: 10.5 secs | LR: 0.000234
INFO:tensorflow:Step 1700 | Loss: 0.7339 | Spent: 10.6 secs | LR: 0.000238
INFO:tensorflow:Step 1750 | Loss: 0.7485 | Spent: 10.3 secs | LR: 0.000243
INFO:tensorflow:Step 1800 | Loss: 0.7946 | Spent: 10.6 secs | LR: 0.000247
INFO:tensorflow:Step 1850 | Loss: 0.7922 | Spent: 10.5 secs | LR: 0.000251
INFO:tensorflow:Step 1900 | Loss: 0.7640 | Spent: 10.6 secs | LR: 0.000255
INFO:tensorflow:Step 1950 | Loss: 0.8544 | Spent: 10.5 secs | LR: 0.000259
INFO:tensorflow:Step 2000 | Loss: 0.7564 | Spent: 10.5 secs | LR: 0.000263
INFO:tensorflow:Step 2050 | Loss: 0.7798 | Spent: 10.6 secs | LR: 0.000267
INFO:tensorflow:Step 2100 | Loss: 0.7669 | Spent: 10.8 secs | LR: 0.000271
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.813
INFO:tensorflow:Best Accuracy: 0.813
Reading ../data/train.txt
INFO:tensorflow:Step 2150 | Loss: 0.7619 | Spent: 45.0 secs | LR: 0.000275
INFO:tensorflow:Step 2200 | Loss: 0.7359 | Spent: 12.7 secs | LR: 0.000279
INFO:tensorflow:Step 2250 | Loss: 0.7716 | Spent: 12.5 secs | LR: 0.000283
INFO:tensorflow:Step 2300 | Loss: 0.7965 | Spent: 12.4 secs | LR: 0.000287
INFO:tensorflow:Step 2350 | Loss: 0.7744 | Spent: 12.7 secs | LR: 0.000291
INFO:tensorflow:Step 2400 | Loss: 0.7395 | Spent: 12.5 secs | LR: 0.000295
INFO:tensorflow:Step 2450 | Loss: 0.7412 | Spent: 12.9 secs | LR: 0.000300
INFO:tensorflow:Step 2500 | Loss: 0.7607 | Spent: 12.7 secs | LR: 0.000304
INFO:tensorflow:Step 2550 | Loss: 0.7435 | Spent: 13.1 secs | LR: 0.000308
INFO:tensorflow:Step 2600 | Loss: 0.7487 | Spent: 12.9 secs | LR: 0.000312
INFO:tensorflow:Step 2650 | Loss: 0.7670 | Spent: 12.7 secs | LR: 0.000316
INFO:tensorflow:Step 2700 | Loss: 0.7528 | Spent: 12.9 secs | LR: 0.000320
INFO:tensorflow:Step 2750 | Loss: 0.7767 | Spent: 12.4 secs | LR: 0.000324
INFO:tensorflow:Step 2800 | Loss: 0.7542 | Spent: 13.1 secs | LR: 0.000328
INFO:tensorflow:Step 2850 | Loss: 0.7871 | Spent: 12.5 secs | LR: 0.000332
INFO:tensorflow:Step 2900 | Loss: 0.7740 | Spent: 12.8 secs | LR: 0.000336
INFO:tensorflow:Step 2950 | Loss: 0.7931 | Spent: 12.4 secs | LR: 0.000340
INFO:tensorflow:Step 3000 | Loss: 0.8069 | Spent: 12.5 secs | LR: 0.000344
INFO:tensorflow:Step 3050 | Loss: 0.7500 | Spent: 13.3 secs | LR: 0.000348
INFO:tensorflow:Step 3100 | Loss: 0.7494 | Spent: 13.0 secs | LR: 0.000352
INFO:tensorflow:Step 3150 | Loss: 0.7394 | Spent: 12.9 secs | LR: 0.000357
INFO:tensorflow:Step 3200 | Loss: 0.7414 | Spent: 12.5 secs | LR: 0.000361
INFO:tensorflow:Step 3250 | Loss: 0.7551 | Spent: 12.7 secs | LR: 0.000365
INFO:tensorflow:Step 3300 | Loss: 0.7082 | Spent: 12.6 secs | LR: 0.000369
INFO:tensorflow:Step 3350 | Loss: 0.7316 | Spent: 12.4 secs | LR: 0.000373
INFO:tensorflow:Step 3400 | Loss: 0.7218 | Spent: 12.5 secs | LR: 0.000377
INFO:tensorflow:Step 3450 | Loss: 0.7365 | Spent: 12.7 secs | LR: 0.000381
INFO:tensorflow:Step 3500 | Loss: 0.7197 | Spent: 12.7 secs | LR: 0.000385
INFO:tensorflow:Step 3550 | Loss: 0.7904 | Spent: 11.1 secs | LR: 0.000389
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INFO:tensorflow:Step 4000 | Loss: 0.7000 | Spent: 10.8 secs | LR: 0.000426
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INFO:tensorflow:Step 4250 | Loss: 0.7225 | Spent: 10.9 secs | LR: 0.000446
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.847
INFO:tensorflow:Best Accuracy: 0.847
Reading ../data/train.txt
INFO:tensorflow:Step 4300 | Loss: 0.7132 | Spent: 45.6 secs | LR: 0.000450
INFO:tensorflow:Step 4350 | Loss: 0.6815 | Spent: 12.7 secs | LR: 0.000454
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INFO:tensorflow:Step 4600 | Loss: 0.6949 | Spent: 12.4 secs | LR: 0.000475
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INFO:tensorflow:Step 6150 | Loss: 0.7342 | Spent: 10.8 secs | LR: 0.000601
INFO:tensorflow:Step 6200 | Loss: 0.7250 | Spent: 10.9 secs | LR: 0.000605
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INFO:tensorflow:Step 6400 | Loss: 0.6968 | Spent: 10.4 secs | LR: 0.000621
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.856
INFO:tensorflow:Best Accuracy: 0.856
Reading ../data/train.txt
INFO:tensorflow:Step 6450 | Loss: 0.7266 | Spent: 45.0 secs | LR: 0.000625
INFO:tensorflow:Step 6500 | Loss: 0.6701 | Spent: 12.5 secs | LR: 0.000629
INFO:tensorflow:Step 6550 | Loss: 0.6759 | Spent: 12.6 secs | LR: 0.000633
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INFO:tensorflow:Step 6850 | Loss: 0.7130 | Spent: 12.6 secs | LR: 0.000658
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INFO:tensorflow:Step 8550 | Loss: 0.6694 | Spent: 10.6 secs | LR: 0.000796
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.863
INFO:tensorflow:Best Accuracy: 0.863
Reading ../data/train.txt
INFO:tensorflow:Step 8600 | Loss: 0.6851 | Spent: 45.5 secs | LR: 0.000800
INFO:tensorflow:Step 8650 | Loss: 0.6747 | Spent: 13.0 secs | LR: 0.000796
INFO:tensorflow:Step 8700 | Loss: 0.6411 | Spent: 12.7 secs | LR: 0.000792
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INFO:tensorflow:Step 8900 | Loss: 0.6942 | Spent: 12.6 secs | LR: 0.000775
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INFO:tensorflow:Step 9000 | Loss: 0.7067 | Spent: 12.8 secs | LR: 0.000767
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INFO:tensorflow:Step 10700 | Loss: 0.6904 | Spent: 10.7 secs | LR: 0.000629
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.870
INFO:tensorflow:Best Accuracy: 0.870
Reading ../data/train.txt
INFO:tensorflow:Step 10750 | Loss: 0.6738 | Spent: 46.0 secs | LR: 0.000625
INFO:tensorflow:Step 10800 | Loss: 0.6615 | Spent: 12.9 secs | LR: 0.000621
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INFO:tensorflow:Step 12250 | Loss: 0.6726 | Spent: 10.9 secs | LR: 0.000502
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INFO:tensorflow:Step 12400 | Loss: 0.6348 | Spent: 10.8 secs | LR: 0.000490
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INFO:tensorflow:Step 12600 | Loss: 0.6605 | Spent: 10.8 secs | LR: 0.000474
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INFO:tensorflow:Step 12850 | Loss: 0.6564 | Spent: 10.7 secs | LR: 0.000454
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.877
INFO:tensorflow:Best Accuracy: 0.877
Reading ../data/train.txt
INFO:tensorflow:Step 12900 | Loss: 0.6219 | Spent: 46.6 secs | LR: 0.000450
INFO:tensorflow:Step 12950 | Loss: 0.6566 | Spent: 13.0 secs | LR: 0.000445
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INFO:tensorflow:Step 14700 | Loss: 0.6389 | Spent: 10.7 secs | LR: 0.000303
INFO:tensorflow:Step 14750 | Loss: 0.6342 | Spent: 10.9 secs | LR: 0.000299
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INFO:tensorflow:Step 15000 | Loss: 0.6355 | Spent: 10.9 secs | LR: 0.000279
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.881
Reading ../data/train.txt
INFO:tensorflow:Step 15050 | Loss: 0.6342 | Spent: 46.2 secs | LR: 0.000274
INFO:tensorflow:Step 15100 | Loss: 0.6366 | Spent: 12.7 secs | LR: 0.000270
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INFO:tensorflow:Step 17150 | Loss: 0.6428 | Spent: 10.3 secs | LR: 0.000103
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.881
Reading ../data/train.txt
INFO:tensorflow:Step 17200 | Loss: 0.6180 | Spent: 47.4 secs | LR: 0.000100
INFO:tensorflow:Step 17250 | Loss: 0.5926 | Spent: 12.6 secs | LR: 0.000102
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INFO:tensorflow:Step 19300 | Loss: 0.6157 | Spent: 10.5 secs | LR: 0.000186
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 19350 | Loss: 0.6031 | Spent: 45.5 secs | LR: 0.000188
INFO:tensorflow:Step 19400 | Loss: 0.5949 | Spent: 12.6 secs | LR: 0.000190
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.877
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 21500 | Loss: 0.6069 | Spent: 46.5 secs | LR: 0.000275
INFO:tensorflow:Step 21550 | Loss: 0.6731 | Spent: 12.7 secs | LR: 0.000277
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INFO:tensorflow:Step 23600 | Loss: 0.5891 | Spent: 10.4 secs | LR: 0.000361
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 23650 | Loss: 0.6077 | Spent: 46.4 secs | LR: 0.000363
INFO:tensorflow:Step 23700 | Loss: 0.5833 | Spent: 12.8 secs | LR: 0.000365
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INFO:tensorflow:Step 25750 | Loss: 0.6069 | Spent: 11.0 secs | LR: 0.000448
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.878
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 25800 | Loss: 0.6148 | Spent: 48.2 secs | LR: 0.000450
INFO:tensorflow:Step 25850 | Loss: 0.6031 | Spent: 12.5 secs | LR: 0.000447
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 27900 | Loss: 0.6179 | Spent: 44.2 secs | LR: 0.000364
INFO:tensorflow:Step 27950 | Loss: 0.6071 | Spent: 12.6 secs | LR: 0.000362
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INFO:tensorflow:Step 30000 | Loss: 0.5846 | Spent: 10.5 secs | LR: 0.000279
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 30050 | Loss: 0.6158 | Spent: 44.8 secs | LR: 0.000276
INFO:tensorflow:Step 30100 | Loss: 0.6306 | Spent: 12.6 secs | LR: 0.000274
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INFO:tensorflow:Step 32150 | Loss: 0.5933 | Spent: 10.9 secs | LR: 0.000191
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 32200 | Loss: 0.6035 | Spent: 44.9 secs | LR: 0.000189
INFO:tensorflow:Step 32250 | Loss: 0.5557 | Spent: 12.8 secs | LR: 0.000187
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INFO:tensorflow:Step 34300 | Loss: 0.5439 | Spent: 10.6 secs | LR: 0.000103
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 34350 | Loss: 0.5785 | Spent: 45.8 secs | LR: 0.000101
INFO:tensorflow:Step 34400 | Loss: 0.6006 | Spent: 12.6 secs | LR: 0.000100
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 36500 | Loss: 0.5627 | Spent: 45.0 secs | LR: 0.000143
INFO:tensorflow:Step 36550 | Loss: 0.5827 | Spent: 12.3 secs | LR: 0.000144
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 38650 | Loss: 0.5739 | Spent: 45.3 secs | LR: 0.000187
INFO:tensorflow:Step 38700 | Loss: 0.5884 | Spent: 12.7 secs | LR: 0.000188
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 40800 | Loss: 0.5546 | Spent: 45.8 secs | LR: 0.000231
INFO:tensorflow:Step 40850 | Loss: 0.6139 | Spent: 12.5 secs | LR: 0.000232
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 42950 | Loss: 0.5668 | Spent: 46.0 secs | LR: 0.000274
INFO:tensorflow:Step 43000 | Loss: 0.5747 | Spent: 12.6 secs | LR: 0.000275
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 45100 | Loss: 0.5845 | Spent: 46.0 secs | LR: 0.000232
INFO:tensorflow:Step 45150 | Loss: 0.5577 | Spent: 12.7 secs | LR: 0.000231
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INFO:tensorflow:Step 47200 | Loss: 0.5637 | Spent: 10.3 secs | LR: 0.000189
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 47250 | Loss: 0.5763 | Spent: 46.4 secs | LR: 0.000188
INFO:tensorflow:Step 47300 | Loss: 0.5729 | Spent: 12.7 secs | LR: 0.000187
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 49400 | Loss: 0.5519 | Spent: 46.8 secs | LR: 0.000144
INFO:tensorflow:Step 49450 | Loss: 0.5596 | Spent: 12.7 secs | LR: 0.000143
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INFO:tensorflow:Step 51500 | Loss: 0.5591 | Spent: 10.5 secs | LR: 0.000102
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 51550 | Loss: 0.5700 | Spent: 46.8 secs | LR: 0.000101
INFO:tensorflow:Step 51600 | Loss: 0.5710 | Spent: 12.5 secs | LR: 0.000100
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INFO:tensorflow:Step 53600 | Loss: 0.5550 | Spent: 10.5 secs | LR: 0.000121
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 53650 | Loss: 0.5318 | Spent: 45.3 secs | LR: 0.000121
INFO:tensorflow:Step 53700 | Loss: 0.5988 | Spent: 12.7 secs | LR: 0.000122
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.882
Reading ../data/train.txt
INFO:tensorflow:Step 55800 | Loss: 0.5291 | Spent: 45.9 secs | LR: 0.000143
INFO:tensorflow:Step 55850 | Loss: 0.5471 | Spent: 12.6 secs | LR: 0.000144
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.883
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 57950 | Loss: 0.5403 | Spent: 45.3 secs | LR: 0.000165
INFO:tensorflow:Step 58000 | Loss: 0.5381 | Spent: 13.1 secs | LR: 0.000165
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 60100 | Loss: 0.5480 | Spent: 45.7 secs | LR: 0.000187
INFO:tensorflow:Step 60150 | Loss: 0.5735 | Spent: 12.8 secs | LR: 0.000187
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 62250 | Loss: 0.5466 | Spent: 46.7 secs | LR: 0.000166
INFO:tensorflow:Step 62300 | Loss: 0.5453 | Spent: 12.7 secs | LR: 0.000166
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.882
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 64400 | Loss: 0.5417 | Spent: 45.2 secs | LR: 0.000144
INFO:tensorflow:Step 64450 | Loss: 0.5323 | Spent: 12.7 secs | LR: 0.000144
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 66550 | Loss: 0.5437 | Spent: 45.6 secs | LR: 0.000123
INFO:tensorflow:Step 66600 | Loss: 0.5568 | Spent: 12.7 secs | LR: 0.000122
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.883
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 68700 | Loss: 0.5333 | Spent: 45.5 secs | LR: 0.000101
INFO:tensorflow:Step 68750 | Loss: 0.5349 | Spent: 12.6 secs | LR: 0.000100
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 70850 | Loss: 0.5322 | Spent: 46.9 secs | LR: 0.000111
INFO:tensorflow:Step 70900 | Loss: 0.5481 | Spent: 12.5 secs | LR: 0.000111
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Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.880
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 73000 | Loss: 0.5379 | Spent: 46.2 secs | LR: 0.000122
INFO:tensorflow:Step 73050 | Loss: 0.5422 | Spent: 12.7 secs | LR: 0.000122
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INFO:tensorflow:Step 74500 | Loss: 0.5461 | Spent: 10.7 secs | LR: 0.000129
INFO:tensorflow:Step 74550 | Loss: 0.5523 | Spent: 10.8 secs | LR: 0.000129
INFO:tensorflow:Step 74600 | Loss: 0.5556 | Spent: 10.5 secs | LR: 0.000130
INFO:tensorflow:Step 74650 | Loss: 0.5589 | Spent: 10.8 secs | LR: 0.000130
INFO:tensorflow:Step 74700 | Loss: 0.5709 | Spent: 10.7 secs | LR: 0.000130
INFO:tensorflow:Step 74750 | Loss: 0.5434 | Spent: 10.6 secs | LR: 0.000130
INFO:tensorflow:Step 74800 | Loss: 0.5491 | Spent: 10.7 secs | LR: 0.000131
INFO:tensorflow:Step 74850 | Loss: 0.5542 | Spent: 10.9 secs | LR: 0.000131
INFO:tensorflow:Step 74900 | Loss: 0.5570 | Spent: 10.6 secs | LR: 0.000131
INFO:tensorflow:Step 74950 | Loss: 0.5633 | Spent: 10.6 secs | LR: 0.000131
INFO:tensorflow:Step 75000 | Loss: 0.5598 | Spent: 10.3 secs | LR: 0.000132
INFO:tensorflow:Step 75050 | Loss: 0.5395 | Spent: 10.7 secs | LR: 0.000132
INFO:tensorflow:Step 75100 | Loss: 0.5162 | Spent: 10.6 secs | LR: 0.000132
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.879
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 75150 | Loss: 0.5257 | Spent: 46.4 secs | LR: 0.000132
INFO:tensorflow:Step 75200 | Loss: 0.5386 | Spent: 12.7 secs | LR: 0.000133
INFO:tensorflow:Step 75250 | Loss: 0.5255 | Spent: 12.5 secs | LR: 0.000133
INFO:tensorflow:Step 75300 | Loss: 0.5313 | Spent: 12.9 secs | LR: 0.000133
INFO:tensorflow:Step 75350 | Loss: 0.5305 | Spent: 12.4 secs | LR: 0.000133
INFO:tensorflow:Step 75400 | Loss: 0.5534 | Spent: 12.9 secs | LR: 0.000134
INFO:tensorflow:Step 75450 | Loss: 0.5312 | Spent: 12.9 secs | LR: 0.000134
INFO:tensorflow:Step 75500 | Loss: 0.5444 | Spent: 12.8 secs | LR: 0.000134
INFO:tensorflow:Step 75550 | Loss: 0.5572 | Spent: 12.6 secs | LR: 0.000135
INFO:tensorflow:Step 75600 | Loss: 0.5642 | Spent: 12.6 secs | LR: 0.000135
INFO:tensorflow:Step 75650 | Loss: 0.5697 | Spent: 12.6 secs | LR: 0.000135
INFO:tensorflow:Step 75700 | Loss: 0.5594 | Spent: 12.9 secs | LR: 0.000135
INFO:tensorflow:Step 75750 | Loss: 0.5199 | Spent: 12.8 secs | LR: 0.000136
INFO:tensorflow:Step 75800 | Loss: 0.5292 | Spent: 12.9 secs | LR: 0.000136
INFO:tensorflow:Step 75850 | Loss: 0.5471 | Spent: 12.6 secs | LR: 0.000136
INFO:tensorflow:Step 75900 | Loss: 0.5395 | Spent: 12.6 secs | LR: 0.000136
INFO:tensorflow:Step 75950 | Loss: 0.5219 | Spent: 12.5 secs | LR: 0.000137
INFO:tensorflow:Step 76000 | Loss: 0.5507 | Spent: 13.1 secs | LR: 0.000137
INFO:tensorflow:Step 76050 | Loss: 0.5451 | Spent: 12.8 secs | LR: 0.000137
INFO:tensorflow:Step 76100 | Loss: 0.5521 | Spent: 12.7 secs | LR: 0.000137
INFO:tensorflow:Step 76150 | Loss: 0.5726 | Spent: 12.4 secs | LR: 0.000138
INFO:tensorflow:Step 76200 | Loss: 0.5586 | Spent: 12.2 secs | LR: 0.000138
INFO:tensorflow:Step 76250 | Loss: 0.5376 | Spent: 12.8 secs | LR: 0.000138
INFO:tensorflow:Step 76300 | Loss: 0.5758 | Spent: 12.3 secs | LR: 0.000138
INFO:tensorflow:Step 76350 | Loss: 0.5574 | Spent: 12.4 secs | LR: 0.000139
INFO:tensorflow:Step 76400 | Loss: 0.5271 | Spent: 12.7 secs | LR: 0.000139
INFO:tensorflow:Step 76450 | Loss: 0.5434 | Spent: 12.8 secs | LR: 0.000139
INFO:tensorflow:Step 76500 | Loss: 0.5557 | Spent: 11.4 secs | LR: 0.000139
INFO:tensorflow:Step 76550 | Loss: 0.5335 | Spent: 10.6 secs | LR: 0.000140
INFO:tensorflow:Step 76600 | Loss: 0.5409 | Spent: 10.5 secs | LR: 0.000140
INFO:tensorflow:Step 76650 | Loss: 0.5430 | Spent: 10.6 secs | LR: 0.000140
INFO:tensorflow:Step 76700 | Loss: 0.5699 | Spent: 10.8 secs | LR: 0.000140
INFO:tensorflow:Step 76750 | Loss: 0.5318 | Spent: 10.9 secs | LR: 0.000141
INFO:tensorflow:Step 76800 | Loss: 0.5244 | Spent: 10.7 secs | LR: 0.000141
INFO:tensorflow:Step 76850 | Loss: 0.5574 | Spent: 10.8 secs | LR: 0.000141
INFO:tensorflow:Step 76900 | Loss: 0.5518 | Spent: 11.0 secs | LR: 0.000141
INFO:tensorflow:Step 76950 | Loss: 0.5149 | Spent: 10.6 secs | LR: 0.000142
INFO:tensorflow:Step 77000 | Loss: 0.5332 | Spent: 10.8 secs | LR: 0.000142
INFO:tensorflow:Step 77050 | Loss: 0.5468 | Spent: 10.5 secs | LR: 0.000142
INFO:tensorflow:Step 77100 | Loss: 0.5755 | Spent: 10.6 secs | LR: 0.000142
INFO:tensorflow:Step 77150 | Loss: 0.5385 | Spent: 10.7 secs | LR: 0.000143
INFO:tensorflow:Step 77200 | Loss: 0.5464 | Spent: 10.5 secs | LR: 0.000143
INFO:tensorflow:Step 77250 | Loss: 0.5468 | Spent: 10.6 secs | LR: 0.000143
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.883
Reading ../data/train.txt
INFO:tensorflow:Step 77300 | Loss: 0.5580 | Spent: 46.5 secs | LR: 0.000143
INFO:tensorflow:Step 77350 | Loss: 0.5337 | Spent: 12.8 secs | LR: 0.000144
INFO:tensorflow:Step 77400 | Loss: 0.5290 | Spent: 12.6 secs | LR: 0.000144
INFO:tensorflow:Step 77450 | Loss: 0.5386 | Spent: 13.0 secs | LR: 0.000143
INFO:tensorflow:Step 77500 | Loss: 0.5574 | Spent: 12.8 secs | LR: 0.000143
INFO:tensorflow:Step 77550 | Loss: 0.5718 | Spent: 12.4 secs | LR: 0.000143
INFO:tensorflow:Step 77600 | Loss: 0.5305 | Spent: 12.6 secs | LR: 0.000143
INFO:tensorflow:Step 77650 | Loss: 0.5591 | Spent: 13.0 secs | LR: 0.000142
INFO:tensorflow:Step 77700 | Loss: 0.5337 | Spent: 12.9 secs | LR: 0.000142
INFO:tensorflow:Step 77750 | Loss: 0.5489 | Spent: 12.8 secs | LR: 0.000142
INFO:tensorflow:Step 77800 | Loss: 0.5516 | Spent: 12.9 secs | LR: 0.000142
INFO:tensorflow:Step 77850 | Loss: 0.5495 | Spent: 12.7 secs | LR: 0.000141
INFO:tensorflow:Step 77900 | Loss: 0.5471 | Spent: 12.7 secs | LR: 0.000141
INFO:tensorflow:Step 77950 | Loss: 0.5402 | Spent: 12.5 secs | LR: 0.000141
INFO:tensorflow:Step 78000 | Loss: 0.5451 | Spent: 13.1 secs | LR: 0.000141
INFO:tensorflow:Step 78050 | Loss: 0.5321 | Spent: 12.9 secs | LR: 0.000140
INFO:tensorflow:Step 78100 | Loss: 0.5681 | Spent: 12.6 secs | LR: 0.000140
INFO:tensorflow:Step 78150 | Loss: 0.5468 | Spent: 12.7 secs | LR: 0.000140
INFO:tensorflow:Step 78200 | Loss: 0.5296 | Spent: 12.7 secs | LR: 0.000139
INFO:tensorflow:Step 78250 | Loss: 0.5597 | Spent: 12.7 secs | LR: 0.000139
INFO:tensorflow:Step 78300 | Loss: 0.5311 | Spent: 12.6 secs | LR: 0.000139
INFO:tensorflow:Step 78350 | Loss: 0.5609 | Spent: 12.8 secs | LR: 0.000139
INFO:tensorflow:Step 78400 | Loss: 0.5750 | Spent: 12.7 secs | LR: 0.000138
INFO:tensorflow:Step 78450 | Loss: 0.5372 | Spent: 13.0 secs | LR: 0.000138
INFO:tensorflow:Step 78500 | Loss: 0.5458 | Spent: 12.7 secs | LR: 0.000138
INFO:tensorflow:Step 78550 | Loss: 0.5382 | Spent: 12.8 secs | LR: 0.000138
INFO:tensorflow:Step 78600 | Loss: 0.5412 | Spent: 12.8 secs | LR: 0.000137
INFO:tensorflow:Step 78650 | Loss: 0.5489 | Spent: 11.2 secs | LR: 0.000137
INFO:tensorflow:Step 78700 | Loss: 0.5300 | Spent: 10.6 secs | LR: 0.000137
INFO:tensorflow:Step 78750 | Loss: 0.5448 | Spent: 10.5 secs | LR: 0.000137
INFO:tensorflow:Step 78800 | Loss: 0.5337 | Spent: 10.7 secs | LR: 0.000136
INFO:tensorflow:Step 78850 | Loss: 0.5328 | Spent: 10.9 secs | LR: 0.000136
INFO:tensorflow:Step 78900 | Loss: 0.5441 | Spent: 10.7 secs | LR: 0.000136
INFO:tensorflow:Step 78950 | Loss: 0.5546 | Spent: 10.6 secs | LR: 0.000136
INFO:tensorflow:Step 79000 | Loss: 0.5450 | Spent: 10.7 secs | LR: 0.000135
INFO:tensorflow:Step 79050 | Loss: 0.5391 | Spent: 10.5 secs | LR: 0.000135
INFO:tensorflow:Step 79100 | Loss: 0.5347 | Spent: 10.3 secs | LR: 0.000135
INFO:tensorflow:Step 79150 | Loss: 0.5427 | Spent: 10.6 secs | LR: 0.000135
INFO:tensorflow:Step 79200 | Loss: 0.5478 | Spent: 10.4 secs | LR: 0.000134
INFO:tensorflow:Step 79250 | Loss: 0.5591 | Spent: 10.6 secs | LR: 0.000134
INFO:tensorflow:Step 79300 | Loss: 0.5812 | Spent: 10.6 secs | LR: 0.000134
INFO:tensorflow:Step 79350 | Loss: 0.5667 | Spent: 10.5 secs | LR: 0.000134
INFO:tensorflow:Step 79400 | Loss: 0.5682 | Spent: 11.0 secs | LR: 0.000133
Reading ../data/test.txt
INFO:tensorflow:Evaluation: Testing Accuracy: 0.881
INFO:tensorflow:Best Accuracy: 0.883
10 times not improve the best result, therefore stop training