In [1]:
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow2/knowledge_graph_completion/wn18/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]:
from tensorflow_addons.optimizers.cyclical_learning_rate import Triangular2CyclicalLearningRate

import tensorflow as tf
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-3-177c62f47bc4>:9: 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]:
"""
we use 1vN fast evaluation as purposed in ConvE paper:
"https://arxiv.org/abs/1707.01476"
sp2o is a dictionary that maps a pair of <subject, predicate>
to multiple possible corresponding <objects> in graph
"""
def make_sp2o(f_paths, e2idx, r2idx):
    sp2o = {}
    for f_path in f_paths:
      with open(f_path) as f:
        for line in f:
            line = line.rstrip()
            s, p, o = line.split()
            s, p, o = e2idx[s], r2idx[p], e2idx[o]
            if (s,p) not in sp2o:
                sp2o[(s,p)] = [o]
            else:
                if o not in sp2o[(s,p)]:
                    sp2o[(s,p)].append(o)
    return sp2o
In [0]:
def map_fn(x, y):
  i, v, s = y[0]
  one_hot = tf.SparseTensor(i, v, s)
  return x, (one_hot, y[1], y[2])


# stream data from text files
def data_generator(f_path, params, sp2o):
  with open(f_path) as f:
    print('Reading', f_path)
    for line in f:
      line = line.rstrip()
      s, p, o = line.split()
      s, p, o = params['e2idx'][s], params['r2idx'][p], params['e2idx'][o]
      sparse_i = [[x] for x in sp2o[(s, p)]]
      sparse_v = [1.] * len(sparse_i)
      sparse_s = [len(params['e2idx'])]
      yield ((s, p), ((sparse_i, sparse_v, sparse_s), o, len(sparse_i)))


def dataset(is_training, params, sp2o):
  _shapes = (([], []), (([None, 1], [None], [1]), [], []))
  _types = ((tf.int32, tf.int32),
            ((tf.int64, tf.float32, tf.int64), tf.int32, tf.int32))
  
  if is_training:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['train_path'], params, sp2o),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.shuffle(params['num_samples'])
    ds = ds.map(map_fn)
    ds = ds.batch(params['batch_size'])
  
  else:
    ds = tf.data.Dataset.from_generator(
      lambda: data_generator(params['test_path'], params, sp2o),
      output_shapes = _shapes,
      output_types = _types,)
    ds = ds.map(map_fn)
    ds = ds.batch(params['batch_size'])
  
  return ds
In [0]:
def update_metrics(scores, query, metrics):
  to_float = lambda x: tf.cast(x, tf.float32)
  
  _, i = tf.math.top_k(scores, sorted=True, k=scores.shape[1])
  query = tf.expand_dims(query, 1)
  is_query = to_float(tf.equal(i, query))
  r = tf.argmax(is_query, -1) + 1
  
  mrr = 1. / to_float(r)
  hits_10 = to_float(tf.less_equal(r, 10))
  hits_3 = to_float(tf.less_equal(r, 3))
  hits_1 = to_float(tf.less_equal(r, 1))
  
  metrics['mrr'].update_state(mrr)
  metrics['hits_10'].update_state(hits_10)
  metrics['hits_3'].update_state(hits_3)
  metrics['hits_1'].update_state(hits_1)
In [0]:
class Complex(tf.keras.Model):
  def __init__(self, params):
    super().__init__()
    self.embed_e_real = tf.keras.layers.Embedding(input_dim=len(params['e2idx']),
                                                  output_dim=params['embed_dim'],
                                                  embeddings_initializer=tf.initializers.RandomUniform(),
                                                  name='Entity_Real')
    
    self.embed_e_img = tf.keras.layers.Embedding(input_dim=len(params['e2idx']),
                                                 output_dim=params['embed_dim'],
                                                 embeddings_initializer=tf.initializers.RandomUniform(),
                                                 name='Entity_Img')
    
    self.embed_rel_real = tf.keras.layers.Embedding(input_dim=len(params['r2idx']),
                                                    output_dim=params['embed_dim'],
                                                    embeddings_initializer=tf.initializers.RandomUniform(),
                                                    name='Relation_Real')
    
    self.embed_rel_img = tf.keras.layers.Embedding(input_dim=len(params['r2idx']),
                                                   output_dim=params['embed_dim'],
                                                   embeddings_initializer=tf.initializers.RandomUniform(),
                                                   name='Relation_Img')
    
    self.out_bias = self.add_weight(name='out_bias', shape=[len(params['e2idx'])])
  
  
  def call(self, inputs):
    s, p = inputs
    
    s_real = self.embed_e_real(s)
    p_real = self.embed_rel_real(p)
    s_img = self.embed_e_img(s)
    p_img = self.embed_rel_img(p)
    
    realrealreal = tf.matmul(s_real*p_real, self.embed_e_real.embeddings, transpose_b=True)
    realimgimg = tf.matmul(s_real*p_img, self.embed_e_img.embeddings, transpose_b=True)
    imgrealimg = tf.matmul(s_img*p_real, self.embed_e_img.embeddings, transpose_b=True)
    imgimgreal = tf.matmul(s_img*p_img, self.embed_e_real.embeddings, transpose_b=True)
    
    x = realrealreal + realimgimg + imgrealimg - imgimgreal
    x = tf.nn.bias_add(x, self.out_bias)
    return x
In [0]:
def label_smoothing(inputs, epsilon):
  V = inputs.get_shape().as_list()[-1]
  return ((1-epsilon) * inputs) + (epsilon / V)
In [0]:
params = {
    'train_path': '../data/wn18/train.txt',
    'valid_path': '../data/wn18/valid.txt',
    'test_path': '../data/wn18/test.txt',
    'entity_path': '../vocab/entity.txt',
    'relation_path': '../vocab/relation.txt',
    'batch_size': 128,
    'embed_dim': 200,
    'num_samples': 141442,
    'init_lr': 1e-4,
    'max_lr': 2e-3,
    'num_patience': 10,
    'epsilon': .1,
}
In [0]:
params['e2idx'] = get_vocab(params['entity_path'])
params['r2idx'] = get_vocab(params['relation_path'])
sp2o_tr = make_sp2o([params['train_path']], params['e2idx'], params['r2idx'])
sp2o_all = make_sp2o([params['train_path'],
                      params['test_path'],
                      params['valid_path']], params['e2idx'], params['r2idx'])
In [12]:
model = Complex(params)
model.build(input_shape=[[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 = 8 * params['num_samples'] // params['batch_size'],)
optim = tf.optimizers.Adam(params['init_lr'])
global_step = 0

best_mrr = 0.
count = 0

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


while True:
  # TRAINING
  for ((s, p), (multi_o, o, num_pos)) in dataset(is_training=True, params=params, sp2o=sp2o_tr):
    with tf.GradientTape() as tape:
      logits = model((s, p))
      multi_o = tf.sparse.to_dense(multi_o, validate_indices=False)
      num_neg = len(params['e2idx']) - num_pos
      pos_weight = tf.expand_dims(tf.cast(num_neg/num_pos, tf.float32), 1)
      labels = label_smoothing(multi_o, params['epsilon'])
      loss = tf.nn.weighted_cross_entropy_with_logits(labels=labels, logits=logits, pos_weight=pos_weight)
      loss = tf.reduce_mean(loss)
    
    optim.lr.assign(decay_lr(global_step))
    grads = tape.gradient(loss, model.trainable_variables)
    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
  metrics = {
    'mrr': tf.metrics.Mean(),
    'hits_10': tf.metrics.Mean(),
    'hits_3': tf.metrics.Mean(),
    'hits_1': tf.metrics.Mean(),
  }
  for ((s, p), (multi_o, o, num_pos)) in dataset(is_training=False, params=params, sp2o=sp2o_all):
    logits = model((s, p))
    multi_o = tf.sparse.to_dense(multi_o, validate_indices=False)
    # create masks for Filtered MRR
    o_one_hot = tf.one_hot(o, len(params['e2idx']))
    unwanted = multi_o - o_one_hot
    masks = tf.cast(tf.equal(unwanted, 0.), tf.float32)
    scores = tf.sigmoid(logits) * masks
    
    update_metrics(scores=scores, query=o, metrics=metrics)
  
  logger.info("MRR: {:.3f}| [email protected]: {:.3f} | [email protected]: {:.3f} | [email protected]: {:.3f}".format(
    metrics['mrr'].result().numpy(),
    metrics['hits_10'].result().numpy(),
    metrics['hits_3'].result().numpy(),
    metrics['hits_1'].result().numpy()))
  
  mrr = metrics['mrr'].result().numpy()
  
  if mrr > best_mrr:
    best_mrr = mrr
    # you can save model here
    count = 0
  else:
    count += 1
  logger.info("Best MRR: {:.3f}".format(best_mrr))
  
  if count == params['num_patience']:
    print(params['num_patience'], "times not improve the best result, therefore stop training")
    break
[('Entity_Real/embeddings:0', TensorShape([40943, 200])),
 ('Entity_Img/embeddings:0', TensorShape([40943, 200])),
 ('Relation_Real/embeddings:0', TensorShape([18, 200])),
 ('Relation_Img/embeddings:0', TensorShape([18, 200])),
 ('out_bias:0', TensorShape([40943]))]
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 0 | Loss: 1.3589 | Spent: 32.8 secs | LR: 0.000100
INFO:tensorflow:Step 50 | Loss: 1.3575 | Spent: 9.2 secs | LR: 0.000111
INFO:tensorflow:Step 100 | Loss: 1.3579 | Spent: 9.1 secs | LR: 0.000121
INFO:tensorflow:Step 150 | Loss: 1.3570 | Spent: 9.1 secs | LR: 0.000132
INFO:tensorflow:Step 200 | Loss: 1.3600 | Spent: 9.2 secs | LR: 0.000143
INFO:tensorflow:Step 250 | Loss: 1.3592 | Spent: 9.2 secs | LR: 0.000154
INFO:tensorflow:Step 300 | Loss: 1.3555 | Spent: 9.2 secs | LR: 0.000164
INFO:tensorflow:Step 350 | Loss: 1.3582 | Spent: 9.2 secs | LR: 0.000175
INFO:tensorflow:Step 400 | Loss: 1.3538 | Spent: 9.2 secs | LR: 0.000186
INFO:tensorflow:Step 450 | Loss: 1.3528 | Spent: 9.2 secs | LR: 0.000197
INFO:tensorflow:Step 500 | Loss: 1.3533 | Spent: 9.2 secs | LR: 0.000207
INFO:tensorflow:Step 550 | Loss: 1.3523 | Spent: 9.2 secs | LR: 0.000218
INFO:tensorflow:Step 600 | Loss: 1.3511 | Spent: 9.2 secs | LR: 0.000229
INFO:tensorflow:Step 650 | Loss: 1.3533 | Spent: 9.2 secs | LR: 0.000240
INFO:tensorflow:Step 700 | Loss: 1.3521 | Spent: 9.2 secs | LR: 0.000250
INFO:tensorflow:Step 750 | Loss: 1.3479 | Spent: 9.2 secs | LR: 0.000261
INFO:tensorflow:Step 800 | Loss: 1.3438 | Spent: 9.2 secs | LR: 0.000272
INFO:tensorflow:Step 850 | Loss: 1.3511 | Spent: 9.2 secs | LR: 0.000283
INFO:tensorflow:Step 900 | Loss: 1.3460 | Spent: 9.2 secs | LR: 0.000293
INFO:tensorflow:Step 950 | Loss: 1.3419 | Spent: 9.2 secs | LR: 0.000304
INFO:tensorflow:Step 1000 | Loss: 1.3479 | Spent: 9.2 secs | LR: 0.000315
INFO:tensorflow:Step 1050 | Loss: 1.3379 | Spent: 9.2 secs | LR: 0.000326
INFO:tensorflow:Step 1100 | Loss: 1.3322 | Spent: 9.2 secs | LR: 0.000336
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.046| [email protected]: 0.057 | [email protected]: 0.043 | [email protected]: 0.039
INFO:tensorflow:Best MRR: 0.046
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 1150 | Loss: 1.3227 | Spent: 46.8 secs | LR: 0.000347
INFO:tensorflow:Step 1200 | Loss: 1.3153 | Spent: 9.2 secs | LR: 0.000358
INFO:tensorflow:Step 1250 | Loss: 1.3027 | Spent: 9.1 secs | LR: 0.000369
INFO:tensorflow:Step 1300 | Loss: 1.2869 | Spent: 9.1 secs | LR: 0.000379
INFO:tensorflow:Step 1350 | Loss: 1.2787 | Spent: 9.2 secs | LR: 0.000390
INFO:tensorflow:Step 1400 | Loss: 1.2523 | Spent: 9.2 secs | LR: 0.000401
INFO:tensorflow:Step 1450 | Loss: 1.2495 | Spent: 9.2 secs | LR: 0.000412
INFO:tensorflow:Step 1500 | Loss: 1.2069 | Spent: 9.2 secs | LR: 0.000422
INFO:tensorflow:Step 1550 | Loss: 1.1675 | Spent: 9.3 secs | LR: 0.000433
INFO:tensorflow:Step 1600 | Loss: 1.1477 | Spent: 9.3 secs | LR: 0.000444
INFO:tensorflow:Step 1650 | Loss: 1.1372 | Spent: 9.2 secs | LR: 0.000455
INFO:tensorflow:Step 1700 | Loss: 1.1017 | Spent: 9.2 secs | LR: 0.000465
INFO:tensorflow:Step 1750 | Loss: 1.0813 | Spent: 9.2 secs | LR: 0.000476
INFO:tensorflow:Step 1800 | Loss: 1.0576 | Spent: 9.2 secs | LR: 0.000487
INFO:tensorflow:Step 1850 | Loss: 1.0195 | Spent: 9.2 secs | LR: 0.000498
INFO:tensorflow:Step 1900 | Loss: 1.0177 | Spent: 9.2 secs | LR: 0.000508
INFO:tensorflow:Step 1950 | Loss: 0.9987 | Spent: 9.2 secs | LR: 0.000519
INFO:tensorflow:Step 2000 | Loss: 0.9639 | Spent: 9.2 secs | LR: 0.000530
INFO:tensorflow:Step 2050 | Loss: 0.9544 | Spent: 9.2 secs | LR: 0.000541
INFO:tensorflow:Step 2100 | Loss: 0.9280 | Spent: 9.2 secs | LR: 0.000551
INFO:tensorflow:Step 2150 | Loss: 0.9059 | Spent: 9.2 secs | LR: 0.000562
INFO:tensorflow:Step 2200 | Loss: 0.8711 | Spent: 9.2 secs | LR: 0.000573
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.435| [email protected]: 0.580 | [email protected]: 0.462 | [email protected]: 0.363
INFO:tensorflow:Best MRR: 0.435
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 2250 | Loss: 0.8255 | Spent: 46.8 secs | LR: 0.000584
INFO:tensorflow:Step 2300 | Loss: 0.8445 | Spent: 9.2 secs | LR: 0.000594
INFO:tensorflow:Step 2350 | Loss: 0.8028 | Spent: 9.2 secs | LR: 0.000605
INFO:tensorflow:Step 2400 | Loss: 0.7886 | Spent: 9.2 secs | LR: 0.000616
INFO:tensorflow:Step 2450 | Loss: 0.7645 | Spent: 9.2 secs | LR: 0.000627
INFO:tensorflow:Step 2500 | Loss: 0.7682 | Spent: 9.2 secs | LR: 0.000637
INFO:tensorflow:Step 2550 | Loss: 0.7416 | Spent: 9.2 secs | LR: 0.000648
INFO:tensorflow:Step 2600 | Loss: 0.7165 | Spent: 9.2 secs | LR: 0.000659
INFO:tensorflow:Step 2650 | Loss: 0.6873 | Spent: 9.2 secs | LR: 0.000670
INFO:tensorflow:Step 2700 | Loss: 0.6768 | Spent: 9.2 secs | LR: 0.000680
INFO:tensorflow:Step 2750 | Loss: 0.6353 | Spent: 9.3 secs | LR: 0.000691
INFO:tensorflow:Step 2800 | Loss: 0.6442 | Spent: 9.2 secs | LR: 0.000702
INFO:tensorflow:Step 2850 | Loss: 0.6066 | Spent: 9.2 secs | LR: 0.000713
INFO:tensorflow:Step 2900 | Loss: 0.5656 | Spent: 9.2 secs | LR: 0.000723
INFO:tensorflow:Step 2950 | Loss: 0.5337 | Spent: 9.2 secs | LR: 0.000734
INFO:tensorflow:Step 3000 | Loss: 0.5458 | Spent: 9.2 secs | LR: 0.000745
INFO:tensorflow:Step 3050 | Loss: 0.4733 | Spent: 9.3 secs | LR: 0.000756
INFO:tensorflow:Step 3100 | Loss: 0.4666 | Spent: 9.2 secs | LR: 0.000766
INFO:tensorflow:Step 3150 | Loss: 0.4764 | Spent: 9.2 secs | LR: 0.000777
INFO:tensorflow:Step 3200 | Loss: 0.4386 | Spent: 9.2 secs | LR: 0.000788
INFO:tensorflow:Step 3250 | Loss: 0.4250 | Spent: 9.2 secs | LR: 0.000799
INFO:tensorflow:Step 3300 | Loss: 0.3787 | Spent: 9.2 secs | LR: 0.000809
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.622| [email protected]: 0.857 | [email protected]: 0.728 | [email protected]: 0.487
INFO:tensorflow:Best MRR: 0.622
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 3350 | Loss: 0.3396 | Spent: 46.5 secs | LR: 0.000820
INFO:tensorflow:Step 3400 | Loss: 0.3184 | Spent: 9.2 secs | LR: 0.000831
INFO:tensorflow:Step 3450 | Loss: 0.3408 | Spent: 9.2 secs | LR: 0.000842
INFO:tensorflow:Step 3500 | Loss: 0.3480 | Spent: 9.2 secs | LR: 0.000852
INFO:tensorflow:Step 3550 | Loss: 0.3133 | Spent: 9.2 secs | LR: 0.000863
INFO:tensorflow:Step 3600 | Loss: 0.2840 | Spent: 9.2 secs | LR: 0.000874
INFO:tensorflow:Step 3650 | Loss: 0.3189 | Spent: 9.2 secs | LR: 0.000885
INFO:tensorflow:Step 3700 | Loss: 0.2988 | Spent: 9.2 secs | LR: 0.000895
INFO:tensorflow:Step 3750 | Loss: 0.2982 | Spent: 9.2 secs | LR: 0.000906
INFO:tensorflow:Step 3800 | Loss: 0.2718 | Spent: 9.2 secs | LR: 0.000917
INFO:tensorflow:Step 3850 | Loss: 0.2657 | Spent: 9.3 secs | LR: 0.000927
INFO:tensorflow:Step 3900 | Loss: 0.2772 | Spent: 9.2 secs | LR: 0.000938
INFO:tensorflow:Step 3950 | Loss: 0.2772 | Spent: 9.2 secs | LR: 0.000949
INFO:tensorflow:Step 4000 | Loss: 0.2871 | Spent: 9.2 secs | LR: 0.000960
INFO:tensorflow:Step 4050 | Loss: 0.2553 | Spent: 9.2 secs | LR: 0.000970
INFO:tensorflow:Step 4100 | Loss: 0.2572 | Spent: 9.2 secs | LR: 0.000981
INFO:tensorflow:Step 4150 | Loss: 0.2518 | Spent: 9.2 secs | LR: 0.000992
INFO:tensorflow:Step 4200 | Loss: 0.2428 | Spent: 9.2 secs | LR: 0.001003
INFO:tensorflow:Step 4250 | Loss: 0.2553 | Spent: 9.2 secs | LR: 0.001013
INFO:tensorflow:Step 4300 | Loss: 0.2380 | Spent: 9.2 secs | LR: 0.001024
INFO:tensorflow:Step 4350 | Loss: 0.2490 | Spent: 9.2 secs | LR: 0.001035
INFO:tensorflow:Step 4400 | Loss: 0.2676 | Spent: 9.2 secs | LR: 0.001046
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.888| [email protected]: 0.942 | [email protected]: 0.920 | [email protected]: 0.850
INFO:tensorflow:Best MRR: 0.888
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 4450 | Loss: 0.2505 | Spent: 47.0 secs | LR: 0.001056
INFO:tensorflow:Step 4500 | Loss: 0.2384 | Spent: 9.0 secs | LR: 0.001067
INFO:tensorflow:Step 4550 | Loss: 0.2354 | Spent: 9.1 secs | LR: 0.001078
INFO:tensorflow:Step 4600 | Loss: 0.2386 | Spent: 9.0 secs | LR: 0.001089
INFO:tensorflow:Step 4650 | Loss: 0.2404 | Spent: 9.0 secs | LR: 0.001099
INFO:tensorflow:Step 4700 | Loss: 0.2320 | Spent: 9.1 secs | LR: 0.001110
INFO:tensorflow:Step 4750 | Loss: 0.2323 | Spent: 9.0 secs | LR: 0.001121
INFO:tensorflow:Step 4800 | Loss: 0.2339 | Spent: 9.1 secs | LR: 0.001132
INFO:tensorflow:Step 4850 | Loss: 0.2396 | Spent: 9.2 secs | LR: 0.001142
INFO:tensorflow:Step 4900 | Loss: 0.2219 | Spent: 9.2 secs | LR: 0.001153
INFO:tensorflow:Step 4950 | Loss: 0.2298 | Spent: 9.2 secs | LR: 0.001164
INFO:tensorflow:Step 5000 | Loss: 0.2223 | Spent: 9.1 secs | LR: 0.001175
INFO:tensorflow:Step 5050 | Loss: 0.2218 | Spent: 9.2 secs | LR: 0.001185
INFO:tensorflow:Step 5100 | Loss: 0.2479 | Spent: 9.2 secs | LR: 0.001196
INFO:tensorflow:Step 5150 | Loss: 0.2456 | Spent: 9.2 secs | LR: 0.001207
INFO:tensorflow:Step 5200 | Loss: 0.2207 | Spent: 9.2 secs | LR: 0.001218
INFO:tensorflow:Step 5250 | Loss: 0.2538 | Spent: 9.2 secs | LR: 0.001228
INFO:tensorflow:Step 5300 | Loss: 0.2391 | Spent: 9.2 secs | LR: 0.001239
INFO:tensorflow:Step 5350 | Loss: 0.2565 | Spent: 9.2 secs | LR: 0.001250
INFO:tensorflow:Step 5400 | Loss: 0.2352 | Spent: 9.2 secs | LR: 0.001261
INFO:tensorflow:Step 5450 | Loss: 0.2339 | Spent: 9.2 secs | LR: 0.001271
INFO:tensorflow:Step 5500 | Loss: 0.2303 | Spent: 9.2 secs | LR: 0.001282
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.927| [email protected]: 0.948 | [email protected]: 0.942 | [email protected]: 0.911
INFO:tensorflow:Best MRR: 0.927
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 5550 | Loss: 0.2254 | Spent: 46.9 secs | LR: 0.001293
INFO:tensorflow:Step 5600 | Loss: 0.2242 | Spent: 9.2 secs | LR: 0.001304
INFO:tensorflow:Step 5650 | Loss: 0.2271 | Spent: 9.2 secs | LR: 0.001314
INFO:tensorflow:Step 5700 | Loss: 0.2250 | Spent: 9.2 secs | LR: 0.001325
INFO:tensorflow:Step 5750 | Loss: 0.2175 | Spent: 9.2 secs | LR: 0.001336
INFO:tensorflow:Step 5800 | Loss: 0.2427 | Spent: 9.2 secs | LR: 0.001347
INFO:tensorflow:Step 5850 | Loss: 0.2134 | Spent: 9.3 secs | LR: 0.001357
INFO:tensorflow:Step 5900 | Loss: 0.2120 | Spent: 9.2 secs | LR: 0.001368
INFO:tensorflow:Step 5950 | Loss: 0.2538 | Spent: 9.2 secs | LR: 0.001379
INFO:tensorflow:Step 6000 | Loss: 0.2095 | Spent: 9.2 secs | LR: 0.001390
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INFO:tensorflow:Step 6150 | Loss: 0.2264 | Spent: 9.2 secs | LR: 0.001422
INFO:tensorflow:Step 6200 | Loss: 0.2385 | Spent: 9.2 secs | LR: 0.001433
INFO:tensorflow:Step 6250 | Loss: 0.2184 | Spent: 9.2 secs | LR: 0.001443
INFO:tensorflow:Step 6300 | Loss: 0.2316 | Spent: 9.2 secs | LR: 0.001454
INFO:tensorflow:Step 6350 | Loss: 0.2097 | Spent: 9.2 secs | LR: 0.001465
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INFO:tensorflow:Step 6500 | Loss: 0.2448 | Spent: 9.2 secs | LR: 0.001497
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INFO:tensorflow:Step 6600 | Loss: 0.2280 | Spent: 9.1 secs | LR: 0.001519
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.937| [email protected]: 0.950 | [email protected]: 0.945 | [email protected]: 0.928
INFO:tensorflow:Best MRR: 0.937
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 6650 | Loss: 0.2175 | Spent: 47.0 secs | LR: 0.001529
INFO:tensorflow:Step 6700 | Loss: 0.2260 | Spent: 9.2 secs | LR: 0.001540
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INFO:tensorflow:Step 6900 | Loss: 0.2355 | Spent: 9.2 secs | LR: 0.001583
INFO:tensorflow:Step 6950 | Loss: 0.2231 | Spent: 9.2 secs | LR: 0.001594
INFO:tensorflow:Step 7000 | Loss: 0.2414 | Spent: 9.2 secs | LR: 0.001605
INFO:tensorflow:Step 7050 | Loss: 0.2054 | Spent: 9.1 secs | LR: 0.001615
INFO:tensorflow:Step 7100 | Loss: 0.2269 | Spent: 9.2 secs | LR: 0.001626
INFO:tensorflow:Step 7150 | Loss: 0.2188 | Spent: 9.2 secs | LR: 0.001637
INFO:tensorflow:Step 7200 | Loss: 0.2432 | Spent: 9.2 secs | LR: 0.001648
INFO:tensorflow:Step 7250 | Loss: 0.2119 | Spent: 9.2 secs | LR: 0.001658
INFO:tensorflow:Step 7300 | Loss: 0.2508 | Spent: 9.3 secs | LR: 0.001669
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INFO:tensorflow:Step 7400 | Loss: 0.2379 | Spent: 9.1 secs | LR: 0.001690
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INFO:tensorflow:Step 7500 | Loss: 0.2430 | Spent: 9.2 secs | LR: 0.001712
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INFO:tensorflow:Step 7600 | Loss: 0.2471 | Spent: 9.2 secs | LR: 0.001733
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INFO:tensorflow:Step 7700 | Loss: 0.2188 | Spent: 9.2 secs | LR: 0.001755
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.938| [email protected]: 0.950 | [email protected]: 0.946 | [email protected]: 0.928
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 7750 | Loss: 0.2258 | Spent: 46.8 secs | LR: 0.001766
INFO:tensorflow:Step 7800 | Loss: 0.2397 | Spent: 9.2 secs | LR: 0.001776
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INFO:tensorflow:Step 8200 | Loss: 0.2158 | Spent: 9.2 secs | LR: 0.001862
INFO:tensorflow:Step 8250 | Loss: 0.2352 | Spent: 9.2 secs | LR: 0.001873
INFO:tensorflow:Step 8300 | Loss: 0.2193 | Spent: 9.2 secs | LR: 0.001884
INFO:tensorflow:Step 8350 | Loss: 0.2241 | Spent: 9.2 secs | LR: 0.001895
INFO:tensorflow:Step 8400 | Loss: 0.2115 | Spent: 9.2 secs | LR: 0.001905
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INFO:tensorflow:Step 8500 | Loss: 0.2428 | Spent: 9.2 secs | LR: 0.001927
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INFO:tensorflow:Step 8600 | Loss: 0.2164 | Spent: 9.2 secs | LR: 0.001948
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INFO:tensorflow:Step 8800 | Loss: 0.2297 | Spent: 9.4 secs | LR: 0.001991
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.928| [email protected]: 0.950 | [email protected]: 0.939 | [email protected]: 0.914
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 8850 | Loss: 0.2249 | Spent: 46.9 secs | LR: 0.001998
INFO:tensorflow:Step 8900 | Loss: 0.2290 | Spent: 9.1 secs | LR: 0.001987
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INFO:tensorflow:Step 9150 | Loss: 0.2193 | Spent: 9.2 secs | LR: 0.001933
INFO:tensorflow:Step 9200 | Loss: 0.2208 | Spent: 9.2 secs | LR: 0.001923
INFO:tensorflow:Step 9250 | Loss: 0.2272 | Spent: 9.2 secs | LR: 0.001912
INFO:tensorflow:Step 9300 | Loss: 0.2434 | Spent: 9.2 secs | LR: 0.001901
INFO:tensorflow:Step 9350 | Loss: 0.2432 | Spent: 9.2 secs | LR: 0.001890
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INFO:tensorflow:Step 9950 | Loss: 0.2287 | Spent: 9.2 secs | LR: 0.001761
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.931| [email protected]: 0.950 | [email protected]: 0.940 | [email protected]: 0.919
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 10000 | Loss: 0.2343 | Spent: 46.9 secs | LR: 0.001751
INFO:tensorflow:Step 10050 | Loss: 0.2491 | Spent: 9.3 secs | LR: 0.001740
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INFO:tensorflow:Step 10300 | Loss: 0.2217 | Spent: 9.2 secs | LR: 0.001686
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INFO:tensorflow:Step 10400 | Loss: 0.2316 | Spent: 9.2 secs | LR: 0.001665
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INFO:tensorflow:Step 10600 | Loss: 0.2169 | Spent: 9.2 secs | LR: 0.001622
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INFO:tensorflow:Step 11050 | Loss: 0.2309 | Spent: 9.2 secs | LR: 0.001525
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.932| [email protected]: 0.950 | [email protected]: 0.941 | [email protected]: 0.920
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 11100 | Loss: 0.2151 | Spent: 46.8 secs | LR: 0.001514
INFO:tensorflow:Step 11150 | Loss: 0.2528 | Spent: 9.2 secs | LR: 0.001504
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INFO:tensorflow:Step 11950 | Loss: 0.2295 | Spent: 9.2 secs | LR: 0.001332
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INFO:tensorflow:Step 12100 | Loss: 0.2138 | Spent: 9.1 secs | LR: 0.001299
INFO:tensorflow:Step 12150 | Loss: 0.2125 | Spent: 9.2 secs | LR: 0.001289
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.933| [email protected]: 0.954 | [email protected]: 0.943 | [email protected]: 0.920
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 12200 | Loss: 0.2209 | Spent: 46.8 secs | LR: 0.001278
INFO:tensorflow:Step 12250 | Loss: 0.2294 | Spent: 9.2 secs | LR: 0.001267
INFO:tensorflow:Step 12300 | Loss: 0.2149 | Spent: 9.2 secs | LR: 0.001256
INFO:tensorflow:Step 12350 | Loss: 0.2315 | Spent: 9.2 secs | LR: 0.001246
INFO:tensorflow:Step 12400 | Loss: 0.2053 | Spent: 9.1 secs | LR: 0.001235
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INFO:tensorflow:Step 12500 | Loss: 0.2350 | Spent: 9.2 secs | LR: 0.001213
INFO:tensorflow:Step 12550 | Loss: 0.2189 | Spent: 9.1 secs | LR: 0.001203
INFO:tensorflow:Step 12600 | Loss: 0.2263 | Spent: 9.2 secs | LR: 0.001192
INFO:tensorflow:Step 12650 | Loss: 0.2187 | Spent: 9.2 secs | LR: 0.001181
INFO:tensorflow:Step 12700 | Loss: 0.2291 | Spent: 9.2 secs | LR: 0.001170
INFO:tensorflow:Step 12750 | Loss: 0.2004 | Spent: 9.2 secs | LR: 0.001160
INFO:tensorflow:Step 12800 | Loss: 0.2226 | Spent: 9.2 secs | LR: 0.001149
INFO:tensorflow:Step 12850 | Loss: 0.2295 | Spent: 9.2 secs | LR: 0.001138
INFO:tensorflow:Step 12900 | Loss: 0.2215 | Spent: 9.2 secs | LR: 0.001127
INFO:tensorflow:Step 12950 | Loss: 0.2024 | Spent: 9.2 secs | LR: 0.001117
INFO:tensorflow:Step 13000 | Loss: 0.2344 | Spent: 9.3 secs | LR: 0.001106
INFO:tensorflow:Step 13050 | Loss: 0.2282 | Spent: 9.3 secs | LR: 0.001095
INFO:tensorflow:Step 13100 | Loss: 0.2362 | Spent: 9.2 secs | LR: 0.001084
INFO:tensorflow:Step 13150 | Loss: 0.2464 | Spent: 9.3 secs | LR: 0.001074
INFO:tensorflow:Step 13200 | Loss: 0.2186 | Spent: 9.2 secs | LR: 0.001063
INFO:tensorflow:Step 13250 | Loss: 0.2160 | Spent: 9.2 secs | LR: 0.001052
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.934| [email protected]: 0.954 | [email protected]: 0.943 | [email protected]: 0.922
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 13300 | Loss: 0.2390 | Spent: 46.8 secs | LR: 0.001041
INFO:tensorflow:Step 13350 | Loss: 0.2156 | Spent: 9.2 secs | LR: 0.001031
INFO:tensorflow:Step 13400 | Loss: 0.2234 | Spent: 9.2 secs | LR: 0.001020
INFO:tensorflow:Step 13450 | Loss: 0.2208 | Spent: 9.2 secs | LR: 0.001009
INFO:tensorflow:Step 13500 | Loss: 0.2220 | Spent: 9.2 secs | LR: 0.000998
INFO:tensorflow:Step 13550 | Loss: 0.2229 | Spent: 9.2 secs | LR: 0.000988
INFO:tensorflow:Step 13600 | Loss: 0.2260 | Spent: 9.2 secs | LR: 0.000977
INFO:tensorflow:Step 13650 | Loss: 0.2391 | Spent: 9.2 secs | LR: 0.000966
INFO:tensorflow:Step 13700 | Loss: 0.2338 | Spent: 9.2 secs | LR: 0.000955
INFO:tensorflow:Step 13750 | Loss: 0.2413 | Spent: 9.2 secs | LR: 0.000945
INFO:tensorflow:Step 13800 | Loss: 0.2141 | Spent: 9.3 secs | LR: 0.000934
INFO:tensorflow:Step 13850 | Loss: 0.2543 | Spent: 9.2 secs | LR: 0.000923
INFO:tensorflow:Step 13900 | Loss: 0.2238 | Spent: 9.2 secs | LR: 0.000912
INFO:tensorflow:Step 13950 | Loss: 0.2311 | Spent: 9.2 secs | LR: 0.000902
INFO:tensorflow:Step 14000 | Loss: 0.2109 | Spent: 9.2 secs | LR: 0.000891
INFO:tensorflow:Step 14050 | Loss: 0.2102 | Spent: 9.2 secs | LR: 0.000880
INFO:tensorflow:Step 14100 | Loss: 0.2250 | Spent: 9.2 secs | LR: 0.000869
INFO:tensorflow:Step 14150 | Loss: 0.2138 | Spent: 9.2 secs | LR: 0.000859
INFO:tensorflow:Step 14200 | Loss: 0.2360 | Spent: 9.2 secs | LR: 0.000848
INFO:tensorflow:Step 14250 | Loss: 0.2248 | Spent: 9.2 secs | LR: 0.000837
INFO:tensorflow:Step 14300 | Loss: 0.2216 | Spent: 9.2 secs | LR: 0.000826
INFO:tensorflow:Step 14350 | Loss: 0.2395 | Spent: 9.2 secs | LR: 0.000816
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.933| [email protected]: 0.955 | [email protected]: 0.943 | [email protected]: 0.919
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 14400 | Loss: 0.2242 | Spent: 47.3 secs | LR: 0.000805
INFO:tensorflow:Step 14450 | Loss: 0.2486 | Spent: 9.2 secs | LR: 0.000794
INFO:tensorflow:Step 14500 | Loss: 0.2254 | Spent: 9.2 secs | LR: 0.000783
INFO:tensorflow:Step 14550 | Loss: 0.2256 | Spent: 9.3 secs | LR: 0.000773
INFO:tensorflow:Step 14600 | Loss: 0.2149 | Spent: 9.2 secs | LR: 0.000762
INFO:tensorflow:Step 14650 | Loss: 0.2239 | Spent: 9.2 secs | LR: 0.000751
INFO:tensorflow:Step 14700 | Loss: 0.2225 | Spent: 9.2 secs | LR: 0.000740
INFO:tensorflow:Step 14750 | Loss: 0.2243 | Spent: 9.2 secs | LR: 0.000730
INFO:tensorflow:Step 14800 | Loss: 0.2306 | Spent: 9.2 secs | LR: 0.000719
INFO:tensorflow:Step 14850 | Loss: 0.2288 | Spent: 9.2 secs | LR: 0.000708
INFO:tensorflow:Step 14900 | Loss: 0.2232 | Spent: 9.2 secs | LR: 0.000698
INFO:tensorflow:Step 14950 | Loss: 0.2393 | Spent: 9.2 secs | LR: 0.000687
INFO:tensorflow:Step 15000 | Loss: 0.2186 | Spent: 9.2 secs | LR: 0.000676
INFO:tensorflow:Step 15050 | Loss: 0.2368 | Spent: 9.2 secs | LR: 0.000665
INFO:tensorflow:Step 15100 | Loss: 0.2334 | Spent: 9.2 secs | LR: 0.000655
INFO:tensorflow:Step 15150 | Loss: 0.2402 | Spent: 9.2 secs | LR: 0.000644
INFO:tensorflow:Step 15200 | Loss: 0.2293 | Spent: 9.2 secs | LR: 0.000633
INFO:tensorflow:Step 15250 | Loss: 0.2056 | Spent: 9.3 secs | LR: 0.000622
INFO:tensorflow:Step 15300 | Loss: 0.2331 | Spent: 9.2 secs | LR: 0.000612
INFO:tensorflow:Step 15350 | Loss: 0.2459 | Spent: 9.2 secs | LR: 0.000601
INFO:tensorflow:Step 15400 | Loss: 0.2189 | Spent: 9.2 secs | LR: 0.000590
INFO:tensorflow:Step 15450 | Loss: 0.2175 | Spent: 9.2 secs | LR: 0.000579
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.937| [email protected]: 0.957 | [email protected]: 0.947 | [email protected]: 0.925
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 15500 | Loss: 0.2217 | Spent: 46.9 secs | LR: 0.000569
INFO:tensorflow:Step 15550 | Loss: 0.2368 | Spent: 9.3 secs | LR: 0.000558
INFO:tensorflow:Step 15600 | Loss: 0.2166 | Spent: 9.2 secs | LR: 0.000547
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INFO:tensorflow:Step 15800 | Loss: 0.2295 | Spent: 9.4 secs | LR: 0.000504
INFO:tensorflow:Step 15850 | Loss: 0.2365 | Spent: 9.3 secs | LR: 0.000493
INFO:tensorflow:Step 15900 | Loss: 0.2255 | Spent: 9.2 secs | LR: 0.000483
INFO:tensorflow:Step 15950 | Loss: 0.2161 | Spent: 9.2 secs | LR: 0.000472
INFO:tensorflow:Step 16000 | Loss: 0.2352 | Spent: 9.3 secs | LR: 0.000461
INFO:tensorflow:Step 16050 | Loss: 0.2160 | Spent: 9.3 secs | LR: 0.000450
INFO:tensorflow:Step 16100 | Loss: 0.2187 | Spent: 9.2 secs | LR: 0.000440
INFO:tensorflow:Step 16150 | Loss: 0.2191 | Spent: 9.1 secs | LR: 0.000429
INFO:tensorflow:Step 16200 | Loss: 0.2119 | Spent: 9.2 secs | LR: 0.000418
INFO:tensorflow:Step 16250 | Loss: 0.2337 | Spent: 9.2 secs | LR: 0.000407
INFO:tensorflow:Step 16300 | Loss: 0.2165 | Spent: 9.2 secs | LR: 0.000397
INFO:tensorflow:Step 16350 | Loss: 0.2500 | Spent: 9.2 secs | LR: 0.000386
INFO:tensorflow:Step 16400 | Loss: 0.2189 | Spent: 9.2 secs | LR: 0.000375
INFO:tensorflow:Step 16450 | Loss: 0.2004 | Spent: 9.2 secs | LR: 0.000364
INFO:tensorflow:Step 16500 | Loss: 0.2245 | Spent: 9.2 secs | LR: 0.000354
INFO:tensorflow:Step 16550 | Loss: 0.2222 | Spent: 9.2 secs | LR: 0.000343
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.938| [email protected]: 0.958 | [email protected]: 0.948 | [email protected]: 0.925
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 16600 | Loss: 0.2234 | Spent: 46.8 secs | LR: 0.000332
INFO:tensorflow:Step 16650 | Loss: 0.2194 | Spent: 9.2 secs | LR: 0.000321
INFO:tensorflow:Step 16700 | Loss: 0.2277 | Spent: 9.2 secs | LR: 0.000311
INFO:tensorflow:Step 16750 | Loss: 0.2311 | Spent: 9.2 secs | LR: 0.000300
INFO:tensorflow:Step 16800 | Loss: 0.2250 | Spent: 9.2 secs | LR: 0.000289
INFO:tensorflow:Step 16850 | Loss: 0.2176 | Spent: 9.2 secs | LR: 0.000278
INFO:tensorflow:Step 16900 | Loss: 0.2155 | Spent: 9.2 secs | LR: 0.000268
INFO:tensorflow:Step 16950 | Loss: 0.2336 | Spent: 9.2 secs | LR: 0.000257
INFO:tensorflow:Step 17000 | Loss: 0.2333 | Spent: 9.2 secs | LR: 0.000246
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INFO:tensorflow:Step 17200 | Loss: 0.2317 | Spent: 9.1 secs | LR: 0.000203
INFO:tensorflow:Step 17250 | Loss: 0.2234 | Spent: 9.2 secs | LR: 0.000192
INFO:tensorflow:Step 17300 | Loss: 0.2274 | Spent: 9.2 secs | LR: 0.000182
INFO:tensorflow:Step 17350 | Loss: 0.2373 | Spent: 9.1 secs | LR: 0.000171
INFO:tensorflow:Step 17400 | Loss: 0.2104 | Spent: 9.1 secs | LR: 0.000160
INFO:tensorflow:Step 17450 | Loss: 0.2203 | Spent: 9.1 secs | LR: 0.000149
INFO:tensorflow:Step 17500 | Loss: 0.2316 | Spent: 9.1 secs | LR: 0.000139
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INFO:tensorflow:Step 17650 | Loss: 0.2224 | Spent: 9.1 secs | LR: 0.000106
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.938| [email protected]: 0.958 | [email protected]: 0.948 | [email protected]: 0.925
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 17700 | Loss: 0.2128 | Spent: 46.5 secs | LR: 0.000102
INFO:tensorflow:Step 17750 | Loss: 0.2128 | Spent: 9.1 secs | LR: 0.000108
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INFO:tensorflow:Step 18000 | Loss: 0.2231 | Spent: 9.1 secs | LR: 0.000134
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INFO:tensorflow:Step 18100 | Loss: 0.2225 | Spent: 9.0 secs | LR: 0.000145
INFO:tensorflow:Step 18150 | Loss: 0.2264 | Spent: 9.1 secs | LR: 0.000151
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INFO:tensorflow:Step 18800 | Loss: 0.2173 | Spent: 9.3 secs | LR: 0.000220
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.937| [email protected]: 0.958 | [email protected]: 0.948 | [email protected]: 0.924
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 18850 | Loss: 0.2385 | Spent: 46.8 secs | LR: 0.000226
INFO:tensorflow:Step 18900 | Loss: 0.1997 | Spent: 9.1 secs | LR: 0.000231
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INFO:tensorflow:Step 19600 | Loss: 0.2346 | Spent: 9.1 secs | LR: 0.000306
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INFO:tensorflow:Step 19900 | Loss: 0.2220 | Spent: 9.1 secs | LR: 0.000339
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.937| [email protected]: 0.958 | [email protected]: 0.949 | [email protected]: 0.923
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 19950 | Loss: 0.2294 | Spent: 46.2 secs | LR: 0.000344
INFO:tensorflow:Step 20000 | Loss: 0.2340 | Spent: 9.1 secs | LR: 0.000349
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INFO:tensorflow:Step 20350 | Loss: 0.2365 | Spent: 9.1 secs | LR: 0.000387
INFO:tensorflow:Step 20400 | Loss: 0.2296 | Spent: 9.1 secs | LR: 0.000392
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INFO:tensorflow:Step 21000 | Loss: 0.2166 | Spent: 9.1 secs | LR: 0.000457
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.936| [email protected]: 0.958 | [email protected]: 0.949 | [email protected]: 0.922
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 21050 | Loss: 0.2096 | Spent: 46.0 secs | LR: 0.000462
INFO:tensorflow:Step 21100 | Loss: 0.2301 | Spent: 9.1 secs | LR: 0.000468
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INFO:tensorflow:Step 22100 | Loss: 0.2125 | Spent: 9.1 secs | LR: 0.000575
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.934| [email protected]: 0.957 | [email protected]: 0.947 | [email protected]: 0.920
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 22150 | Loss: 0.2194 | Spent: 46.0 secs | LR: 0.000580
INFO:tensorflow:Step 22200 | Loss: 0.2117 | Spent: 9.0 secs | LR: 0.000586
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INFO:tensorflow:Step 23200 | Loss: 0.2426 | Spent: 9.0 secs | LR: 0.000693
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.930| [email protected]: 0.956 | [email protected]: 0.944 | [email protected]: 0.914
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 23250 | Loss: 0.2171 | Spent: 44.7 secs | LR: 0.000699
INFO:tensorflow:Step 23300 | Loss: 0.2225 | Spent: 9.0 secs | LR: 0.000704
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INFO:tensorflow:Step 24150 | Loss: 0.2019 | Spent: 9.0 secs | LR: 0.000795
INFO:tensorflow:Step 24200 | Loss: 0.2248 | Spent: 9.0 secs | LR: 0.000801
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INFO:tensorflow:Step 24300 | Loss: 0.2279 | Spent: 9.1 secs | LR: 0.000811
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.928| [email protected]: 0.957 | [email protected]: 0.943 | [email protected]: 0.910
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 24350 | Loss: 0.2254 | Spent: 44.9 secs | LR: 0.000817
INFO:tensorflow:Step 24400 | Loss: 0.2163 | Spent: 9.0 secs | LR: 0.000822
INFO:tensorflow:Step 24450 | Loss: 0.2182 | Spent: 9.1 secs | LR: 0.000828
INFO:tensorflow:Step 24500 | Loss: 0.2207 | Spent: 9.0 secs | LR: 0.000833
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INFO:tensorflow:Step 24600 | Loss: 0.2189 | Spent: 9.0 secs | LR: 0.000844
INFO:tensorflow:Step 24650 | Loss: 0.2247 | Spent: 9.0 secs | LR: 0.000849
INFO:tensorflow:Step 24700 | Loss: 0.2236 | Spent: 8.9 secs | LR: 0.000854
INFO:tensorflow:Step 24750 | Loss: 0.2219 | Spent: 9.0 secs | LR: 0.000860
INFO:tensorflow:Step 24800 | Loss: 0.2110 | Spent: 9.0 secs | LR: 0.000865
INFO:tensorflow:Step 24850 | Loss: 0.2286 | Spent: 9.0 secs | LR: 0.000871
INFO:tensorflow:Step 24900 | Loss: 0.2324 | Spent: 9.0 secs | LR: 0.000876
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INFO:tensorflow:Step 25000 | Loss: 0.2191 | Spent: 9.0 secs | LR: 0.000887
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INFO:tensorflow:Step 25100 | Loss: 0.2417 | Spent: 9.0 secs | LR: 0.000897
INFO:tensorflow:Step 25150 | Loss: 0.2283 | Spent: 9.0 secs | LR: 0.000903
INFO:tensorflow:Step 25200 | Loss: 0.2324 | Spent: 9.0 secs | LR: 0.000908
INFO:tensorflow:Step 25250 | Loss: 0.2164 | Spent: 9.0 secs | LR: 0.000914
INFO:tensorflow:Step 25300 | Loss: 0.2313 | Spent: 9.0 secs | LR: 0.000919
INFO:tensorflow:Step 25350 | Loss: 0.2182 | Spent: 9.0 secs | LR: 0.000924
INFO:tensorflow:Step 25400 | Loss: 0.2061 | Spent: 9.0 secs | LR: 0.000930
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.928| [email protected]: 0.955 | [email protected]: 0.943 | [email protected]: 0.911
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 25450 | Loss: 0.2125 | Spent: 44.8 secs | LR: 0.000935
INFO:tensorflow:Step 25500 | Loss: 0.2395 | Spent: 9.0 secs | LR: 0.000940
INFO:tensorflow:Step 25550 | Loss: 0.2421 | Spent: 9.0 secs | LR: 0.000946
INFO:tensorflow:Step 25600 | Loss: 0.2423 | Spent: 9.0 secs | LR: 0.000951
INFO:tensorflow:Step 25650 | Loss: 0.2105 | Spent: 9.0 secs | LR: 0.000957
INFO:tensorflow:Step 25700 | Loss: 0.2257 | Spent: 9.0 secs | LR: 0.000962
INFO:tensorflow:Step 25750 | Loss: 0.2400 | Spent: 9.0 secs | LR: 0.000967
INFO:tensorflow:Step 25800 | Loss: 0.2230 | Spent: 9.0 secs | LR: 0.000973
INFO:tensorflow:Step 25850 | Loss: 0.2254 | Spent: 9.0 secs | LR: 0.000978
INFO:tensorflow:Step 25900 | Loss: 0.2157 | Spent: 8.9 secs | LR: 0.000983
INFO:tensorflow:Step 25950 | Loss: 0.2299 | Spent: 9.1 secs | LR: 0.000989
INFO:tensorflow:Step 26000 | Loss: 0.2369 | Spent: 9.0 secs | LR: 0.000994
INFO:tensorflow:Step 26050 | Loss: 0.2272 | Spent: 9.0 secs | LR: 0.000999
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INFO:tensorflow:Step 26200 | Loss: 0.2274 | Spent: 9.0 secs | LR: 0.001016
INFO:tensorflow:Step 26250 | Loss: 0.2326 | Spent: 9.0 secs | LR: 0.001021
INFO:tensorflow:Step 26300 | Loss: 0.2250 | Spent: 9.0 secs | LR: 0.001026
INFO:tensorflow:Step 26350 | Loss: 0.2208 | Spent: 9.0 secs | LR: 0.001032
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INFO:tensorflow:Step 26500 | Loss: 0.2160 | Spent: 9.0 secs | LR: 0.001048
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.925| [email protected]: 0.953 | [email protected]: 0.939 | [email protected]: 0.908
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 26550 | Loss: 0.2242 | Spent: 44.6 secs | LR: 0.001047
INFO:tensorflow:Step 26600 | Loss: 0.2446 | Spent: 8.9 secs | LR: 0.001041
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INFO:tensorflow:Step 26950 | Loss: 0.2328 | Spent: 8.9 secs | LR: 0.001004
INFO:tensorflow:Step 27000 | Loss: 0.2232 | Spent: 8.9 secs | LR: 0.000998
INFO:tensorflow:Step 27050 | Loss: 0.2078 | Spent: 8.9 secs | LR: 0.000993
INFO:tensorflow:Step 27100 | Loss: 0.2223 | Spent: 8.9 secs | LR: 0.000988
INFO:tensorflow:Step 27150 | Loss: 0.2495 | Spent: 8.9 secs | LR: 0.000982
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INFO:tensorflow:Step 27250 | Loss: 0.2266 | Spent: 8.9 secs | LR: 0.000972
INFO:tensorflow:Step 27300 | Loss: 0.2197 | Spent: 8.9 secs | LR: 0.000966
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INFO:tensorflow:Step 27600 | Loss: 0.2607 | Spent: 8.9 secs | LR: 0.000934
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.924| [email protected]: 0.954 | [email protected]: 0.940 | [email protected]: 0.905
INFO:tensorflow:Best MRR: 0.938
Reading ../data/wn18/train.txt
INFO:tensorflow:Step 27650 | Loss: 0.2199 | Spent: 43.9 secs | LR: 0.000929
INFO:tensorflow:Step 27700 | Loss: 0.2098 | Spent: 8.9 secs | LR: 0.000923
INFO:tensorflow:Step 27750 | Loss: 0.2276 | Spent: 8.9 secs | LR: 0.000918
INFO:tensorflow:Step 27800 | Loss: 0.2119 | Spent: 9.0 secs | LR: 0.000912
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INFO:tensorflow:Step 28000 | Loss: 0.2304 | Spent: 9.0 secs | LR: 0.000891
INFO:tensorflow:Step 28050 | Loss: 0.2165 | Spent: 8.9 secs | LR: 0.000886
INFO:tensorflow:Step 28100 | Loss: 0.2229 | Spent: 8.9 secs | LR: 0.000880
INFO:tensorflow:Step 28150 | Loss: 0.2243 | Spent: 8.9 secs | LR: 0.000875
INFO:tensorflow:Step 28200 | Loss: 0.2396 | Spent: 8.9 secs | LR: 0.000869
INFO:tensorflow:Step 28250 | Loss: 0.2403 | Spent: 8.9 secs | LR: 0.000864
INFO:tensorflow:Step 28300 | Loss: 0.2190 | Spent: 8.9 secs | LR: 0.000859
INFO:tensorflow:Step 28350 | Loss: 0.2142 | Spent: 8.9 secs | LR: 0.000853
INFO:tensorflow:Step 28400 | Loss: 0.2214 | Spent: 8.9 secs | LR: 0.000848
INFO:tensorflow:Step 28450 | Loss: 0.2212 | Spent: 8.9 secs | LR: 0.000843
INFO:tensorflow:Step 28500 | Loss: 0.2130 | Spent: 8.9 secs | LR: 0.000837
INFO:tensorflow:Step 28550 | Loss: 0.2080 | Spent: 8.9 secs | LR: 0.000832
INFO:tensorflow:Step 28600 | Loss: 0.2348 | Spent: 8.9 secs | LR: 0.000826
INFO:tensorflow:Step 28650 | Loss: 0.2170 | Spent: 8.9 secs | LR: 0.000821
INFO:tensorflow:Step 28700 | Loss: 0.2429 | Spent: 8.9 secs | LR: 0.000816
INFO:tensorflow:Step 28750 | Loss: 0.2147 | Spent: 8.9 secs | LR: 0.000810
Reading ../data/wn18/test.txt
INFO:tensorflow:MRR: 0.926| [email protected]: 0.955 | [email protected]: 0.941 | [email protected]: 0.908
INFO:tensorflow:Best MRR: 0.938
10 times not improve the best result, therefore stop training