"""
We use following lines because we are running on Google Colab
If you are running notebook on a local computer, you don't need these
"""
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).
!pip install tensorflow-gpu==2.0.0-alpha0
Requirement already satisfied: tensorflow-gpu==2.0.0-alpha0 in /usr/local/lib/python3.6/dist-packages (2.0.0a0) Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0) Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.15.0) Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1) Requirement already satisfied: tb-nightly<1.14.0a20190302,>=1.14.0a20190301 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0a20190301) Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.1) Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1) Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2) Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7) Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.11.0) Requirement already satisfied: tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.14.0.dev2019030115) Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.16.2) Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.9) Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1) Requirement already satisfied: google-pasta>=0.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.1.5) Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha0) (40.9.0) Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (3.1) Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (0.15.2) Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha0) (2.8.0)
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 2.0.0-alpha0 GPU Enabled: True
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
"""
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
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
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)
class DistMult(tf.keras.Model):
def __init__(self, params):
super().__init__()
self.embed_ent = tf.keras.layers.Embedding(input_dim=len(params['e2idx']),
output_dim=params['embed_dim'],
embeddings_initializer=tf.initializers.RandomUniform(),
name='Entity')
self.embed_rel = tf.keras.layers.Embedding(input_dim=len(params['r2idx']),
output_dim=params['embed_dim'],
embeddings_initializer=tf.initializers.RandomUniform(),
name='Relation')
def call(self, inputs):
s, p = inputs
s = self.embed_ent(s)
p = self.embed_rel(p)
x = tf.matmul(s * p, self.embed_ent.embeddings, transpose_b=True)
return x
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,
'lr': 3e-3,
'num_patience': 3,
}
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'])
def is_descending(history: list):
history = history[-(params['num_patience']+1):]
for i in range(1, len(history)):
if history[i-1] <= history[i]:
return False
return True
model = DistMult(params)
model.build(input_shape=[[None], [None]])
pprint.pprint([(v.name, v.shape) for v in model.trainable_variables])
decay_lr = tf.optimizers.schedules.ExponentialDecay(params['lr'], 1000, 0.96)
optim = tf.optimizers.Adam(params['lr'])
global_step = 0
best_mrr = 0.
history_mrr = []
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)
loss = tf.nn.weighted_cross_entropy_with_logits(targets=multi_o, 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()
history_mrr.append(mrr)
if mrr > best_mrr:
best_mrr = mrr
# you can save model here
logger.info("Best MRR: {:.3f}".format(best_mrr))
if len(history_mrr) > params['num_patience'] and is_descending(history_mrr):
logger.info("MRR not improved over {} epochs, Early Stop".format(params['num_patience']))
break
WARNING: Logging before flag parsing goes to stderr. W0419 02:45:29.234123 140363059820416 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py:410: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version. Instructions for updating: tf.py_func is deprecated in TF V2. Instead, there are two options available in V2. - tf.py_function takes a python function which manipulates tf eager tensors instead of numpy arrays. It's easy to convert a tf eager tensor to an ndarray (just call tensor.numpy()) but having access to eager tensors means `tf.py_function`s can use accelerators such as GPUs as well as being differentiable using a gradient tape. - tf.numpy_function maintains the semantics of the deprecated tf.py_func (it is not differentiable, and manipulates numpy arrays). It drops the stateful argument making all functions stateful.
[('Entity/embeddings:0', TensorShape([40943, 200])), ('Relation/embeddings:0', TensorShape([18, 200]))] Reading ../data/wn18/train.txt
I0419 02:45:53.790063 140363059820416 interactiveshell.py:2882] Step 0 | Loss: 1.3855 | Spent: 24.6 secs | LR: 0.003000 I0419 02:45:55.924041 140363059820416 interactiveshell.py:2882] Step 50 | Loss: 1.3857 | Spent: 2.1 secs | LR: 0.002994 I0419 02:45:58.058234 140363059820416 interactiveshell.py:2882] Step 100 | Loss: 1.3845 | Spent: 2.1 secs | LR: 0.002988 I0419 02:46:00.185530 140363059820416 interactiveshell.py:2882] Step 150 | Loss: 1.3740 | Spent: 2.1 secs | LR: 0.002982 I0419 02:46:02.306592 140363059820416 interactiveshell.py:2882] Step 200 | Loss: 1.3110 | Spent: 2.1 secs | LR: 0.002976 I0419 02:46:04.441041 140363059820416 interactiveshell.py:2882] Step 250 | Loss: 1.2251 | Spent: 2.1 secs | LR: 0.002970 I0419 02:46:06.568732 140363059820416 interactiveshell.py:2882] Step 300 | Loss: 1.1427 | Spent: 2.1 secs | LR: 0.002963 I0419 02:46:08.724704 140363059820416 interactiveshell.py:2882] Step 350 | Loss: 1.0426 | Spent: 2.2 secs | LR: 0.002957 I0419 02:46:10.869930 140363059820416 interactiveshell.py:2882] Step 400 | Loss: 0.9648 | Spent: 2.1 secs | LR: 0.002951 I0419 02:46:13.017762 140363059820416 interactiveshell.py:2882] Step 450 | Loss: 1.0101 | Spent: 2.1 secs | LR: 0.002945 I0419 02:46:15.150521 140363059820416 interactiveshell.py:2882] Step 500 | Loss: 0.8092 | Spent: 2.1 secs | LR: 0.002939 I0419 02:46:17.301398 140363059820416 interactiveshell.py:2882] Step 550 | Loss: 0.7173 | Spent: 2.1 secs | LR: 0.002933 I0419 02:46:19.447122 140363059820416 interactiveshell.py:2882] Step 600 | Loss: 0.7011 | Spent: 2.1 secs | LR: 0.002927 I0419 02:46:21.585452 140363059820416 interactiveshell.py:2882] Step 650 | Loss: 0.7359 | Spent: 2.1 secs | LR: 0.002921 I0419 02:46:23.719987 140363059820416 interactiveshell.py:2882] Step 700 | Loss: 0.6895 | Spent: 2.1 secs | LR: 0.002915 I0419 02:46:25.868222 140363059820416 interactiveshell.py:2882] Step 750 | Loss: 0.5740 | Spent: 2.1 secs | LR: 0.002910 I0419 02:46:28.003318 140363059820416 interactiveshell.py:2882] Step 800 | Loss: 0.6470 | Spent: 2.1 secs | LR: 0.002904 I0419 02:46:30.146912 140363059820416 interactiveshell.py:2882] Step 850 | Loss: 0.6495 | Spent: 2.1 secs | LR: 0.002898 I0419 02:46:32.295840 140363059820416 interactiveshell.py:2882] Step 900 | Loss: 0.4021 | Spent: 2.1 secs | LR: 0.002892 I0419 02:46:34.440737 140363059820416 interactiveshell.py:2882] Step 950 | Loss: 0.5676 | Spent: 2.1 secs | LR: 0.002886 I0419 02:46:36.585713 140363059820416 interactiveshell.py:2882] Step 1000 | Loss: 0.4147 | Spent: 2.1 secs | LR: 0.002880 I0419 02:46:38.756368 140363059820416 interactiveshell.py:2882] Step 1050 | Loss: 0.4684 | Spent: 2.2 secs | LR: 0.002874 I0419 02:46:41.098708 140363059820416 interactiveshell.py:2882] Step 1100 | Loss: 0.3574 | Spent: 2.3 secs | LR: 0.002868
Reading ../data/wn18/test.txt
I0419 02:46:43.659612 140363059820416 interactiveshell.py:2882] MRR: 0.442| [email protected]: 0.702 | [email protected]: 0.519 | [email protected]: 0.313 I0419 02:46:43.661445 140363059820416 interactiveshell.py:2882] Best MRR: 0.442
Reading ../data/wn18/train.txt
I0419 02:47:13.397497 140363059820416 interactiveshell.py:2882] Step 1150 | Loss: 0.2037 | Spent: 32.3 secs | LR: 0.002862 I0419 02:47:15.514809 140363059820416 interactiveshell.py:2882] Step 1200 | Loss: 0.1545 | Spent: 2.1 secs | LR: 0.002857 I0419 02:47:17.639808 140363059820416 interactiveshell.py:2882] Step 1250 | Loss: 0.1337 | Spent: 2.1 secs | LR: 0.002851 I0419 02:47:19.779346 140363059820416 interactiveshell.py:2882] Step 1300 | Loss: 0.1231 | Spent: 2.1 secs | LR: 0.002845 I0419 02:47:21.912069 140363059820416 interactiveshell.py:2882] Step 1350 | Loss: 0.1442 | Spent: 2.1 secs | LR: 0.002839 I0419 02:47:24.037053 140363059820416 interactiveshell.py:2882] Step 1400 | Loss: 0.1231 | Spent: 2.1 secs | LR: 0.002833 I0419 02:47:26.162008 140363059820416 interactiveshell.py:2882] Step 1450 | Loss: 0.1732 | Spent: 2.1 secs | LR: 0.002828 I0419 02:47:28.295783 140363059820416 interactiveshell.py:2882] Step 1500 | Loss: 0.0875 | Spent: 2.1 secs | LR: 0.002822 I0419 02:47:30.424008 140363059820416 interactiveshell.py:2882] Step 1550 | Loss: 0.2169 | Spent: 2.1 secs | LR: 0.002816 I0419 02:47:32.559748 140363059820416 interactiveshell.py:2882] Step 1600 | Loss: 0.1545 | Spent: 2.1 secs | LR: 0.002810 I0419 02:47:34.685464 140363059820416 interactiveshell.py:2882] Step 1650 | Loss: 0.0682 | Spent: 2.1 secs | LR: 0.002805 I0419 02:47:36.812308 140363059820416 interactiveshell.py:2882] Step 1700 | Loss: 0.1072 | Spent: 2.1 secs | LR: 0.002799 I0419 02:47:38.969979 140363059820416 interactiveshell.py:2882] Step 1750 | Loss: 0.0998 | Spent: 2.2 secs | LR: 0.002793 I0419 02:47:41.103491 140363059820416 interactiveshell.py:2882] Step 1800 | Loss: 0.0542 | Spent: 2.1 secs | LR: 0.002787 I0419 02:47:43.244930 140363059820416 interactiveshell.py:2882] Step 1850 | Loss: 0.0641 | Spent: 2.1 secs | LR: 0.002782 I0419 02:47:45.383041 140363059820416 interactiveshell.py:2882] Step 1900 | Loss: 0.0569 | Spent: 2.1 secs | LR: 0.002776 I0419 02:47:47.515271 140363059820416 interactiveshell.py:2882] Step 1950 | Loss: 0.0466 | Spent: 2.1 secs | LR: 0.002770 I0419 02:47:49.652942 140363059820416 interactiveshell.py:2882] Step 2000 | Loss: 0.0406 | Spent: 2.1 secs | LR: 0.002765 I0419 02:47:51.785445 140363059820416 interactiveshell.py:2882] Step 2050 | Loss: 0.0570 | Spent: 2.1 secs | LR: 0.002759 I0419 02:47:53.930615 140363059820416 interactiveshell.py:2882] Step 2100 | Loss: 0.0511 | Spent: 2.1 secs | LR: 0.002754 I0419 02:47:56.070626 140363059820416 interactiveshell.py:2882] Step 2150 | Loss: 0.0358 | Spent: 2.1 secs | LR: 0.002748 I0419 02:47:58.220481 140363059820416 interactiveshell.py:2882] Step 2200 | Loss: 0.0319 | Spent: 2.1 secs | LR: 0.002742
Reading ../data/wn18/test.txt
I0419 02:48:01.006850 140363059820416 interactiveshell.py:2882] MRR: 0.699| [email protected]: 0.899 | [email protected]: 0.819 | [email protected]: 0.564 I0419 02:48:01.012896 140363059820416 interactiveshell.py:2882] Best MRR: 0.699
Reading ../data/wn18/train.txt
I0419 02:48:28.844053 140363059820416 interactiveshell.py:2882] Step 2250 | Loss: 0.0227 | Spent: 30.6 secs | LR: 0.002737 I0419 02:48:30.969196 140363059820416 interactiveshell.py:2882] Step 2300 | Loss: 0.0178 | Spent: 2.1 secs | LR: 0.002731 I0419 02:48:33.094399 140363059820416 interactiveshell.py:2882] Step 2350 | Loss: 0.0204 | Spent: 2.1 secs | LR: 0.002726 I0419 02:48:35.234266 140363059820416 interactiveshell.py:2882] Step 2400 | Loss: 0.0196 | Spent: 2.1 secs | LR: 0.002720 I0419 02:48:37.367260 140363059820416 interactiveshell.py:2882] Step 2450 | Loss: 0.0169 | Spent: 2.1 secs | LR: 0.002714 I0419 02:48:39.505978 140363059820416 interactiveshell.py:2882] Step 2500 | Loss: 0.0166 | Spent: 2.1 secs | LR: 0.002709 I0419 02:48:41.660887 140363059820416 interactiveshell.py:2882] Step 2550 | Loss: 0.0134 | Spent: 2.2 secs | LR: 0.002703 I0419 02:48:43.804085 140363059820416 interactiveshell.py:2882] Step 2600 | Loss: 0.0136 | Spent: 2.1 secs | LR: 0.002698 I0419 02:48:45.940457 140363059820416 interactiveshell.py:2882] Step 2650 | Loss: 0.0251 | Spent: 2.1 secs | LR: 0.002692 I0419 02:48:48.078678 140363059820416 interactiveshell.py:2882] Step 2700 | Loss: 0.0121 | Spent: 2.1 secs | LR: 0.002687 I0419 02:48:50.227930 140363059820416 interactiveshell.py:2882] Step 2750 | Loss: 0.0135 | Spent: 2.1 secs | LR: 0.002681 I0419 02:48:52.351986 140363059820416 interactiveshell.py:2882] Step 2800 | Loss: 0.0114 | Spent: 2.1 secs | LR: 0.002676 I0419 02:48:54.491408 140363059820416 interactiveshell.py:2882] Step 2850 | Loss: 0.0094 | Spent: 2.1 secs | LR: 0.002671 I0419 02:48:56.622768 140363059820416 interactiveshell.py:2882] Step 2900 | Loss: 0.0114 | Spent: 2.1 secs | LR: 0.002665 I0419 02:48:58.762818 140363059820416 interactiveshell.py:2882] Step 2950 | Loss: 0.0147 | Spent: 2.1 secs | LR: 0.002660 I0419 02:49:00.930675 140363059820416 interactiveshell.py:2882] Step 3000 | Loss: 0.0076 | Spent: 2.2 secs | LR: 0.002654 I0419 02:49:03.078073 140363059820416 interactiveshell.py:2882] Step 3050 | Loss: 0.0113 | Spent: 2.1 secs | LR: 0.002649 I0419 02:49:05.223946 140363059820416 interactiveshell.py:2882] Step 3100 | Loss: 0.0094 | Spent: 2.1 secs | LR: 0.002643 I0419 02:49:07.382345 140363059820416 interactiveshell.py:2882] Step 3150 | Loss: 0.0075 | Spent: 2.2 secs | LR: 0.002638 I0419 02:49:09.537650 140363059820416 interactiveshell.py:2882] Step 3200 | Loss: 0.0075 | Spent: 2.2 secs | LR: 0.002633 I0419 02:49:11.678789 140363059820416 interactiveshell.py:2882] Step 3250 | Loss: 0.0080 | Spent: 2.1 secs | LR: 0.002627 I0419 02:49:13.816834 140363059820416 interactiveshell.py:2882] Step 3300 | Loss: 0.0075 | Spent: 2.1 secs | LR: 0.002622
Reading ../data/wn18/test.txt
I0419 02:49:16.556100 140363059820416 interactiveshell.py:2882] MRR: 0.768| [email protected]: 0.930 | [email protected]: 0.875 | [email protected]: 0.655 I0419 02:49:16.560619 140363059820416 interactiveshell.py:2882] Best MRR: 0.768
Reading ../data/wn18/train.txt
I0419 02:49:45.059677 140363059820416 interactiveshell.py:2882] Step 3350 | Loss: 0.0061 | Spent: 31.2 secs | LR: 0.002617 I0419 02:49:47.203936 140363059820416 interactiveshell.py:2882] Step 3400 | Loss: 0.0059 | Spent: 2.1 secs | LR: 0.002611 I0419 02:49:49.359980 140363059820416 interactiveshell.py:2882] Step 3450 | Loss: 0.0054 | Spent: 2.2 secs | LR: 0.002606 I0419 02:49:51.499655 140363059820416 interactiveshell.py:2882] Step 3500 | Loss: 0.0074 | Spent: 2.1 secs | LR: 0.002601 I0419 02:49:53.649007 140363059820416 interactiveshell.py:2882] Step 3550 | Loss: 0.0050 | Spent: 2.1 secs | LR: 0.002595 I0419 02:49:55.790234 140363059820416 interactiveshell.py:2882] Step 3600 | Loss: 0.0055 | Spent: 2.1 secs | LR: 0.002590 I0419 02:49:57.946289 140363059820416 interactiveshell.py:2882] Step 3650 | Loss: 0.0045 | Spent: 2.2 secs | LR: 0.002585 I0419 02:50:00.102192 140363059820416 interactiveshell.py:2882] Step 3700 | Loss: 0.0052 | Spent: 2.2 secs | LR: 0.002579 I0419 02:50:02.248070 140363059820416 interactiveshell.py:2882] Step 3750 | Loss: 0.0046 | Spent: 2.1 secs | LR: 0.002574 I0419 02:50:04.404830 140363059820416 interactiveshell.py:2882] Step 3800 | Loss: 0.0041 | Spent: 2.2 secs | LR: 0.002569 I0419 02:50:06.574953 140363059820416 interactiveshell.py:2882] Step 3850 | Loss: 0.0037 | Spent: 2.2 secs | LR: 0.002564 I0419 02:50:08.731313 140363059820416 interactiveshell.py:2882] Step 3900 | Loss: 0.0043 | Spent: 2.2 secs | LR: 0.002558 I0419 02:50:10.902874 140363059820416 interactiveshell.py:2882] Step 3950 | Loss: 0.0051 | Spent: 2.2 secs | LR: 0.002553 I0419 02:50:13.098269 140363059820416 interactiveshell.py:2882] Step 4000 | Loss: 0.0038 | Spent: 2.2 secs | LR: 0.002548 I0419 02:50:15.251761 140363059820416 interactiveshell.py:2882] Step 4050 | Loss: 0.0031 | Spent: 2.2 secs | LR: 0.002543 I0419 02:50:17.412610 140363059820416 interactiveshell.py:2882] Step 4100 | Loss: 0.0029 | Spent: 2.2 secs | LR: 0.002538 I0419 02:50:19.578361 140363059820416 interactiveshell.py:2882] Step 4150 | Loss: 0.0033 | Spent: 2.2 secs | LR: 0.002532 I0419 02:50:21.746677 140363059820416 interactiveshell.py:2882] Step 4200 | Loss: 0.0029 | Spent: 2.2 secs | LR: 0.002527 I0419 02:50:23.909059 140363059820416 interactiveshell.py:2882] Step 4250 | Loss: 0.0032 | Spent: 2.2 secs | LR: 0.002522 I0419 02:50:26.063997 140363059820416 interactiveshell.py:2882] Step 4300 | Loss: 0.0036 | Spent: 2.2 secs | LR: 0.002517 I0419 02:50:28.218514 140363059820416 interactiveshell.py:2882] Step 4350 | Loss: 0.0026 | Spent: 2.2 secs | LR: 0.002512 I0419 02:50:30.384402 140363059820416 interactiveshell.py:2882] Step 4400 | Loss: 0.0036 | Spent: 2.2 secs | LR: 0.002507
Reading ../data/wn18/test.txt
I0419 02:50:33.402980 140363059820416 interactiveshell.py:2882] MRR: 0.787| [email protected]: 0.941 | [email protected]: 0.901 | [email protected]: 0.670 I0419 02:50:33.408011 140363059820416 interactiveshell.py:2882] Best MRR: 0.787
Reading ../data/wn18/train.txt
I0419 02:51:01.010845 140363059820416 interactiveshell.py:2882] Step 4450 | Loss: 0.0028 | Spent: 30.6 secs | LR: 0.002502 I0419 02:51:03.167533 140363059820416 interactiveshell.py:2882] Step 4500 | Loss: 0.0024 | Spent: 2.2 secs | LR: 0.002497 I0419 02:51:05.307936 140363059820416 interactiveshell.py:2882] Step 4550 | Loss: 0.0023 | Spent: 2.1 secs | LR: 0.002491 I0419 02:51:07.460746 140363059820416 interactiveshell.py:2882] Step 4600 | Loss: 0.0025 | Spent: 2.2 secs | LR: 0.002486 I0419 02:51:09.626403 140363059820416 interactiveshell.py:2882] Step 4650 | Loss: 0.0019 | Spent: 2.2 secs | LR: 0.002481 I0419 02:51:11.796262 140363059820416 interactiveshell.py:2882] Step 4700 | Loss: 0.0017 | Spent: 2.2 secs | LR: 0.002476 I0419 02:51:13.951525 140363059820416 interactiveshell.py:2882] Step 4750 | Loss: 0.0022 | Spent: 2.2 secs | LR: 0.002471 I0419 02:51:16.119417 140363059820416 interactiveshell.py:2882] Step 4800 | Loss: 0.0025 | Spent: 2.2 secs | LR: 0.002466 I0419 02:51:18.288289 140363059820416 interactiveshell.py:2882] Step 4850 | Loss: 0.0021 | Spent: 2.2 secs | LR: 0.002461 I0419 02:51:20.456620 140363059820416 interactiveshell.py:2882] Step 4900 | Loss: 0.0019 | Spent: 2.2 secs | LR: 0.002456 I0419 02:51:22.623989 140363059820416 interactiveshell.py:2882] Step 4950 | Loss: 0.0018 | Spent: 2.2 secs | LR: 0.002451 I0419 02:51:24.779106 140363059820416 interactiveshell.py:2882] Step 5000 | Loss: 0.0018 | Spent: 2.2 secs | LR: 0.002446 I0419 02:51:26.955220 140363059820416 interactiveshell.py:2882] Step 5050 | Loss: 0.0015 | Spent: 2.2 secs | LR: 0.002441 I0419 02:51:29.128236 140363059820416 interactiveshell.py:2882] Step 5100 | Loss: 0.0019 | Spent: 2.2 secs | LR: 0.002436 I0419 02:51:31.296869 140363059820416 interactiveshell.py:2882] Step 5150 | Loss: 0.0018 | Spent: 2.2 secs | LR: 0.002431 I0419 02:51:33.461788 140363059820416 interactiveshell.py:2882] Step 5200 | Loss: 0.0018 | Spent: 2.2 secs | LR: 0.002426 I0419 02:51:35.616897 140363059820416 interactiveshell.py:2882] Step 5250 | Loss: 0.0017 | Spent: 2.2 secs | LR: 0.002421 I0419 02:51:37.794231 140363059820416 interactiveshell.py:2882] Step 5300 | Loss: 0.0019 | Spent: 2.2 secs | LR: 0.002416 I0419 02:51:39.955643 140363059820416 interactiveshell.py:2882] Step 5350 | Loss: 0.0017 | Spent: 2.2 secs | LR: 0.002411 I0419 02:51:42.136529 140363059820416 interactiveshell.py:2882] Step 5400 | Loss: 0.0020 | Spent: 2.2 secs | LR: 0.002407 I0419 02:51:44.277934 140363059820416 interactiveshell.py:2882] Step 5450 | Loss: 0.0015 | Spent: 2.1 secs | LR: 0.002402 I0419 02:51:46.433987 140363059820416 interactiveshell.py:2882] Step 5500 | Loss: 0.0016 | Spent: 2.2 secs | LR: 0.002397
Reading ../data/wn18/test.txt
I0419 02:51:49.668187 140363059820416 interactiveshell.py:2882] MRR: 0.796| [email protected]: 0.944 | [email protected]: 0.915 | [email protected]: 0.676 I0419 02:51:49.672987 140363059820416 interactiveshell.py:2882] Best MRR: 0.796
Reading ../data/wn18/train.txt
I0419 02:52:18.927590 140363059820416 interactiveshell.py:2882] Step 5550 | Loss: 0.0014 | Spent: 32.5 secs | LR: 0.002392 I0419 02:52:21.358655 140363059820416 interactiveshell.py:2882] Step 5600 | Loss: 0.0012 | Spent: 2.4 secs | LR: 0.002387 I0419 02:52:23.517156 140363059820416 interactiveshell.py:2882] Step 5650 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.002382 I0419 02:52:25.668445 140363059820416 interactiveshell.py:2882] Step 5700 | Loss: 0.0012 | Spent: 2.1 secs | LR: 0.002377 I0419 02:52:27.827868 140363059820416 interactiveshell.py:2882] Step 5750 | Loss: 0.0014 | Spent: 2.2 secs | LR: 0.002372 I0419 02:52:29.998427 140363059820416 interactiveshell.py:2882] Step 5800 | Loss: 0.0013 | Spent: 2.2 secs | LR: 0.002368 I0419 02:52:32.160956 140363059820416 interactiveshell.py:2882] Step 5850 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002363 I0419 02:52:34.322265 140363059820416 interactiveshell.py:2882] Step 5900 | Loss: 0.0013 | Spent: 2.2 secs | LR: 0.002358 I0419 02:52:36.474354 140363059820416 interactiveshell.py:2882] Step 5950 | Loss: 0.0011 | Spent: 2.2 secs | LR: 0.002353 I0419 02:52:38.625435 140363059820416 interactiveshell.py:2882] Step 6000 | Loss: 0.0009 | Spent: 2.1 secs | LR: 0.002348 I0419 02:52:40.784707 140363059820416 interactiveshell.py:2882] Step 6050 | Loss: 0.0011 | Spent: 2.2 secs | LR: 0.002343 I0419 02:52:42.958753 140363059820416 interactiveshell.py:2882] Step 6100 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.002339 I0419 02:52:45.122363 140363059820416 interactiveshell.py:2882] Step 6150 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002334 I0419 02:52:47.288640 140363059820416 interactiveshell.py:2882] Step 6200 | Loss: 0.0011 | Spent: 2.2 secs | LR: 0.002329 I0419 02:52:49.458727 140363059820416 interactiveshell.py:2882] Step 6250 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.002324 I0419 02:52:51.636865 140363059820416 interactiveshell.py:2882] Step 6300 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.002320 I0419 02:52:53.803817 140363059820416 interactiveshell.py:2882] Step 6350 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002315 I0419 02:52:55.966971 140363059820416 interactiveshell.py:2882] Step 6400 | Loss: 0.0013 | Spent: 2.2 secs | LR: 0.002310 I0419 02:52:58.118885 140363059820416 interactiveshell.py:2882] Step 6450 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002306 I0419 02:53:00.285209 140363059820416 interactiveshell.py:2882] Step 6500 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.002301 I0419 02:53:02.454388 140363059820416 interactiveshell.py:2882] Step 6550 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.002296 I0419 02:53:04.616501 140363059820416 interactiveshell.py:2882] Step 6600 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002291
Reading ../data/wn18/test.txt
I0419 02:53:08.135100 140363059820416 interactiveshell.py:2882] MRR: 0.796| [email protected]: 0.945 | [email protected]: 0.918 | [email protected]: 0.671 I0419 02:53:08.139744 140363059820416 interactiveshell.py:2882] Best MRR: 0.796
Reading ../data/wn18/train.txt
I0419 02:53:35.355734 140363059820416 interactiveshell.py:2882] Step 6650 | Loss: 0.0007 | Spent: 30.7 secs | LR: 0.002287 I0419 02:53:37.505830 140363059820416 interactiveshell.py:2882] Step 6700 | Loss: 0.0008 | Spent: 2.1 secs | LR: 0.002282 I0419 02:53:39.654364 140363059820416 interactiveshell.py:2882] Step 6750 | Loss: 0.0010 | Spent: 2.1 secs | LR: 0.002277 I0419 02:53:41.815752 140363059820416 interactiveshell.py:2882] Step 6800 | Loss: 0.0008 | Spent: 2.2 secs | LR: 0.002273 I0419 02:53:43.988122 140363059820416 interactiveshell.py:2882] Step 6850 | Loss: 0.0008 | Spent: 2.2 secs | LR: 0.002268 I0419 02:53:46.137506 140363059820416 interactiveshell.py:2882] Step 6900 | Loss: 0.0007 | Spent: 2.1 secs | LR: 0.002264 I0419 02:53:48.303936 140363059820416 interactiveshell.py:2882] Step 6950 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002259 I0419 02:53:50.477420 140363059820416 interactiveshell.py:2882] Step 7000 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002254 I0419 02:53:52.635059 140363059820416 interactiveshell.py:2882] Step 7050 | Loss: 0.0008 | Spent: 2.2 secs | LR: 0.002250 I0419 02:53:54.805500 140363059820416 interactiveshell.py:2882] Step 7100 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002245 I0419 02:53:56.976144 140363059820416 interactiveshell.py:2882] Step 7150 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002241 I0419 02:53:59.153827 140363059820416 interactiveshell.py:2882] Step 7200 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002236 I0419 02:54:01.332658 140363059820416 interactiveshell.py:2882] Step 7250 | Loss: 0.0008 | Spent: 2.2 secs | LR: 0.002231 I0419 02:54:03.504027 140363059820416 interactiveshell.py:2882] Step 7300 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.002227 I0419 02:54:05.670512 140363059820416 interactiveshell.py:2882] Step 7350 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002222 I0419 02:54:07.842494 140363059820416 interactiveshell.py:2882] Step 7400 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002218 I0419 02:54:10.010387 140363059820416 interactiveshell.py:2882] Step 7450 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002213 I0419 02:54:12.210930 140363059820416 interactiveshell.py:2882] Step 7500 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002209 I0419 02:54:14.388682 140363059820416 interactiveshell.py:2882] Step 7550 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002204 I0419 02:54:16.567562 140363059820416 interactiveshell.py:2882] Step 7600 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002200 I0419 02:54:18.729226 140363059820416 interactiveshell.py:2882] Step 7650 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002195 I0419 02:54:20.885736 140363059820416 interactiveshell.py:2882] Step 7700 | Loss: 0.0008 | Spent: 2.2 secs | LR: 0.002191
Reading ../data/wn18/test.txt
I0419 02:54:24.643871 140363059820416 interactiveshell.py:2882] MRR: 0.796| [email protected]: 0.946 | [email protected]: 0.921 | [email protected]: 0.670 I0419 02:54:24.646815 140363059820416 interactiveshell.py:2882] Best MRR: 0.796
Reading ../data/wn18/train.txt
I0419 02:54:52.752736 140363059820416 interactiveshell.py:2882] Step 7750 | Loss: 0.0005 | Spent: 31.9 secs | LR: 0.002186 I0419 02:54:54.943394 140363059820416 interactiveshell.py:2882] Step 7800 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002182 I0419 02:54:57.096084 140363059820416 interactiveshell.py:2882] Step 7850 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002177 I0419 02:54:59.239016 140363059820416 interactiveshell.py:2882] Step 7900 | Loss: 0.0004 | Spent: 2.1 secs | LR: 0.002173 I0419 02:55:01.402220 140363059820416 interactiveshell.py:2882] Step 7950 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002169 I0419 02:55:03.581848 140363059820416 interactiveshell.py:2882] Step 8000 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002164 I0419 02:55:05.743314 140363059820416 interactiveshell.py:2882] Step 8050 | Loss: 0.0006 | Spent: 2.2 secs | LR: 0.002160 I0419 02:55:07.902896 140363059820416 interactiveshell.py:2882] Step 8100 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002155 I0419 02:55:10.072140 140363059820416 interactiveshell.py:2882] Step 8150 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002151 I0419 02:55:12.260429 140363059820416 interactiveshell.py:2882] Step 8200 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002147 I0419 02:55:14.430076 140363059820416 interactiveshell.py:2882] Step 8250 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002142 I0419 02:55:16.592216 140363059820416 interactiveshell.py:2882] Step 8300 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002138 I0419 02:55:18.749949 140363059820416 interactiveshell.py:2882] Step 8350 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002133 I0419 02:55:20.909818 140363059820416 interactiveshell.py:2882] Step 8400 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002129 I0419 02:55:23.075429 140363059820416 interactiveshell.py:2882] Step 8450 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002125 I0419 02:55:25.243824 140363059820416 interactiveshell.py:2882] Step 8500 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002120 I0419 02:55:27.417875 140363059820416 interactiveshell.py:2882] Step 8550 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002116 I0419 02:55:29.586658 140363059820416 interactiveshell.py:2882] Step 8600 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002112 I0419 02:55:31.748242 140363059820416 interactiveshell.py:2882] Step 8650 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002107 I0419 02:55:33.912075 140363059820416 interactiveshell.py:2882] Step 8700 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002103 I0419 02:55:36.074780 140363059820416 interactiveshell.py:2882] Step 8750 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002099 I0419 02:55:38.230186 140363059820416 interactiveshell.py:2882] Step 8800 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002095
Reading ../data/wn18/test.txt
I0419 02:55:42.252061 140363059820416 interactiveshell.py:2882] MRR: 0.801| [email protected]: 0.947 | [email protected]: 0.924 | [email protected]: 0.677 I0419 02:55:42.256847 140363059820416 interactiveshell.py:2882] Best MRR: 0.801
Reading ../data/wn18/train.txt
I0419 02:56:08.363279 140363059820416 interactiveshell.py:2882] Step 8850 | Loss: 0.0004 | Spent: 30.1 secs | LR: 0.002090 I0419 02:56:10.773043 140363059820416 interactiveshell.py:2882] Step 8900 | Loss: 0.0004 | Spent: 2.4 secs | LR: 0.002086 I0419 02:56:13.042905 140363059820416 interactiveshell.py:2882] Step 8950 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.002082 I0419 02:56:15.192581 140363059820416 interactiveshell.py:2882] Step 9000 | Loss: 0.0004 | Spent: 2.1 secs | LR: 0.002078 I0419 02:56:17.369715 140363059820416 interactiveshell.py:2882] Step 9050 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.002073 I0419 02:56:19.540726 140363059820416 interactiveshell.py:2882] Step 9100 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.002069 I0419 02:56:21.705480 140363059820416 interactiveshell.py:2882] Step 9150 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002065 I0419 02:56:23.876171 140363059820416 interactiveshell.py:2882] Step 9200 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.002061 I0419 02:56:26.050717 140363059820416 interactiveshell.py:2882] Step 9250 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002057 I0419 02:56:28.223433 140363059820416 interactiveshell.py:2882] Step 9300 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002052 I0419 02:56:30.396097 140363059820416 interactiveshell.py:2882] Step 9350 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002048 I0419 02:56:32.580787 140363059820416 interactiveshell.py:2882] Step 9400 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002044 I0419 02:56:34.769355 140363059820416 interactiveshell.py:2882] Step 9450 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.002040 I0419 02:56:36.940620 140363059820416 interactiveshell.py:2882] Step 9500 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002036 I0419 02:56:39.109008 140363059820416 interactiveshell.py:2882] Step 9550 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002031 I0419 02:56:41.276306 140363059820416 interactiveshell.py:2882] Step 9600 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.002027 I0419 02:56:43.451981 140363059820416 interactiveshell.py:2882] Step 9650 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002023 I0419 02:56:45.633667 140363059820416 interactiveshell.py:2882] Step 9700 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002019 I0419 02:56:47.809221 140363059820416 interactiveshell.py:2882] Step 9750 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002015 I0419 02:56:49.976487 140363059820416 interactiveshell.py:2882] Step 9800 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002011 I0419 02:56:52.156462 140363059820416 interactiveshell.py:2882] Step 9850 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.002007 I0419 02:56:54.328870 140363059820416 interactiveshell.py:2882] Step 9900 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.002003 I0419 02:56:56.492269 140363059820416 interactiveshell.py:2882] Step 9950 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001999
Reading ../data/wn18/test.txt
I0419 02:56:58.652274 140363059820416 interactiveshell.py:2882] MRR: 0.802| [email protected]: 0.948 | [email protected]: 0.928 | [email protected]: 0.677 I0419 02:56:58.657151 140363059820416 interactiveshell.py:2882] Best MRR: 0.802
Reading ../data/wn18/train.txt
I0419 02:57:26.162566 140363059820416 interactiveshell.py:2882] Step 10000 | Loss: 0.0003 | Spent: 29.7 secs | LR: 0.001994 I0419 02:57:28.637170 140363059820416 interactiveshell.py:2882] Step 10050 | Loss: 0.0003 | Spent: 2.5 secs | LR: 0.001990 I0419 02:57:31.075687 140363059820416 interactiveshell.py:2882] Step 10100 | Loss: 0.0004 | Spent: 2.4 secs | LR: 0.001986 I0419 02:57:33.342544 140363059820416 interactiveshell.py:2882] Step 10150 | Loss: 0.0004 | Spent: 2.3 secs | LR: 0.001982 I0419 02:57:35.493125 140363059820416 interactiveshell.py:2882] Step 10200 | Loss: 0.0003 | Spent: 2.1 secs | LR: 0.001978 I0419 02:57:37.656020 140363059820416 interactiveshell.py:2882] Step 10250 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001974 I0419 02:57:39.814240 140363059820416 interactiveshell.py:2882] Step 10300 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001970 I0419 02:57:41.972389 140363059820416 interactiveshell.py:2882] Step 10350 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001966 I0419 02:57:44.154650 140363059820416 interactiveshell.py:2882] Step 10400 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001962 I0419 02:57:46.347544 140363059820416 interactiveshell.py:2882] Step 10450 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001958 I0419 02:57:48.528957 140363059820416 interactiveshell.py:2882] Step 10500 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001954 I0419 02:57:50.714782 140363059820416 interactiveshell.py:2882] Step 10550 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001950 I0419 02:57:52.873775 140363059820416 interactiveshell.py:2882] Step 10600 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001946 I0419 02:57:55.043900 140363059820416 interactiveshell.py:2882] Step 10650 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001942 I0419 02:57:57.219841 140363059820416 interactiveshell.py:2882] Step 10700 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001938 I0419 02:57:59.389407 140363059820416 interactiveshell.py:2882] Step 10750 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001934 I0419 02:58:01.572642 140363059820416 interactiveshell.py:2882] Step 10800 | Loss: 0.0009 | Spent: 2.2 secs | LR: 0.001930 I0419 02:58:03.745435 140363059820416 interactiveshell.py:2882] Step 10850 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001926 I0419 02:58:05.922014 140363059820416 interactiveshell.py:2882] Step 10900 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001923 I0419 02:58:08.090964 140363059820416 interactiveshell.py:2882] Step 10950 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001919 I0419 02:58:10.268933 140363059820416 interactiveshell.py:2882] Step 11000 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001915 I0419 02:58:12.457862 140363059820416 interactiveshell.py:2882] Step 11050 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001911
Reading ../data/wn18/test.txt
I0419 02:58:14.920981 140363059820416 interactiveshell.py:2882] MRR: 0.804| [email protected]: 0.947 | [email protected]: 0.924 | [email protected]: 0.682 I0419 02:58:14.925927 140363059820416 interactiveshell.py:2882] Best MRR: 0.804
Reading ../data/wn18/train.txt
I0419 02:58:41.230911 140363059820416 interactiveshell.py:2882] Step 11100 | Loss: 0.0003 | Spent: 28.8 secs | LR: 0.001907 I0419 02:58:43.463304 140363059820416 interactiveshell.py:2882] Step 11150 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001903 I0419 02:58:45.821015 140363059820416 interactiveshell.py:2882] Step 11200 | Loss: 0.0004 | Spent: 2.4 secs | LR: 0.001899 I0419 02:58:48.154664 140363059820416 interactiveshell.py:2882] Step 11250 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001895 I0419 02:58:50.500640 140363059820416 interactiveshell.py:2882] Step 11300 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001891 I0419 02:58:52.819999 140363059820416 interactiveshell.py:2882] Step 11350 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001888 I0419 02:58:54.992840 140363059820416 interactiveshell.py:2882] Step 11400 | Loss: 0.0007 | Spent: 2.2 secs | LR: 0.001884 I0419 02:58:57.152767 140363059820416 interactiveshell.py:2882] Step 11450 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001880 I0419 02:58:59.309727 140363059820416 interactiveshell.py:2882] Step 11500 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001876 I0419 02:59:01.479920 140363059820416 interactiveshell.py:2882] Step 11550 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001872 I0419 02:59:03.649392 140363059820416 interactiveshell.py:2882] Step 11600 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001868 I0419 02:59:05.810759 140363059820416 interactiveshell.py:2882] Step 11650 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001865 I0419 02:59:07.979834 140363059820416 interactiveshell.py:2882] Step 11700 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001861 I0419 02:59:10.150264 140363059820416 interactiveshell.py:2882] Step 11750 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001857 I0419 02:59:12.304775 140363059820416 interactiveshell.py:2882] Step 11800 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001853 I0419 02:59:14.492609 140363059820416 interactiveshell.py:2882] Step 11850 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001849 I0419 02:59:16.669432 140363059820416 interactiveshell.py:2882] Step 11900 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001846 I0419 02:59:18.842114 140363059820416 interactiveshell.py:2882] Step 11950 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001842 I0419 02:59:21.005463 140363059820416 interactiveshell.py:2882] Step 12000 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001838 I0419 02:59:23.171277 140363059820416 interactiveshell.py:2882] Step 12050 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001834 I0419 02:59:25.331317 140363059820416 interactiveshell.py:2882] Step 12100 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001831 I0419 02:59:27.498985 140363059820416 interactiveshell.py:2882] Step 12150 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001827
Reading ../data/wn18/test.txt
I0419 02:59:30.156344 140363059820416 interactiveshell.py:2882] MRR: 0.807| [email protected]: 0.948 | [email protected]: 0.924 | [email protected]: 0.688 I0419 02:59:30.161260 140363059820416 interactiveshell.py:2882] Best MRR: 0.807
Reading ../data/wn18/train.txt
I0419 02:59:57.363820 140363059820416 interactiveshell.py:2882] Step 12200 | Loss: 0.0003 | Spent: 29.9 secs | LR: 0.001823 I0419 02:59:59.542338 140363059820416 interactiveshell.py:2882] Step 12250 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001819 I0419 03:00:01.701362 140363059820416 interactiveshell.py:2882] Step 12300 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001816 I0419 03:00:03.939495 140363059820416 interactiveshell.py:2882] Step 12350 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001812 I0419 03:00:06.288684 140363059820416 interactiveshell.py:2882] Step 12400 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001808 I0419 03:00:08.636584 140363059820416 interactiveshell.py:2882] Step 12450 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001805 I0419 03:00:10.991637 140363059820416 interactiveshell.py:2882] Step 12500 | Loss: 0.0003 | Spent: 2.4 secs | LR: 0.001801 I0419 03:00:13.313622 140363059820416 interactiveshell.py:2882] Step 12550 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001797 I0419 03:00:15.479029 140363059820416 interactiveshell.py:2882] Step 12600 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001794 I0419 03:00:17.649770 140363059820416 interactiveshell.py:2882] Step 12650 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001790 I0419 03:00:19.813188 140363059820416 interactiveshell.py:2882] Step 12700 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001786 I0419 03:00:21.977955 140363059820416 interactiveshell.py:2882] Step 12750 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001783 I0419 03:00:24.156149 140363059820416 interactiveshell.py:2882] Step 12800 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001779 I0419 03:00:26.322770 140363059820416 interactiveshell.py:2882] Step 12850 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001775 I0419 03:00:28.501571 140363059820416 interactiveshell.py:2882] Step 12900 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001772 I0419 03:00:30.664682 140363059820416 interactiveshell.py:2882] Step 12950 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001768 I0419 03:00:32.851950 140363059820416 interactiveshell.py:2882] Step 13000 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001765 I0419 03:00:35.019503 140363059820416 interactiveshell.py:2882] Step 13050 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001761 I0419 03:00:37.180516 140363059820416 interactiveshell.py:2882] Step 13100 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001757 I0419 03:00:39.349562 140363059820416 interactiveshell.py:2882] Step 13150 | Loss: 0.0005 | Spent: 2.2 secs | LR: 0.001754 I0419 03:00:41.515085 140363059820416 interactiveshell.py:2882] Step 13200 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001750 I0419 03:00:43.678015 140363059820416 interactiveshell.py:2882] Step 13250 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001747
Reading ../data/wn18/test.txt
I0419 03:00:46.672073 140363059820416 interactiveshell.py:2882] MRR: 0.808| [email protected]: 0.949 | [email protected]: 0.927 | [email protected]: 0.687 I0419 03:00:46.678630 140363059820416 interactiveshell.py:2882] Best MRR: 0.808
Reading ../data/wn18/train.txt
I0419 03:01:12.565281 140363059820416 interactiveshell.py:2882] Step 13300 | Loss: 0.0002 | Spent: 28.9 secs | LR: 0.001743 I0419 03:01:14.714180 140363059820416 interactiveshell.py:2882] Step 13350 | Loss: 0.0003 | Spent: 2.1 secs | LR: 0.001740 I0419 03:01:16.867226 140363059820416 interactiveshell.py:2882] Step 13400 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001736 I0419 03:01:19.010283 140363059820416 interactiveshell.py:2882] Step 13450 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001732 I0419 03:01:21.177013 140363059820416 interactiveshell.py:2882] Step 13500 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001729 I0419 03:01:23.329146 140363059820416 interactiveshell.py:2882] Step 13550 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001725 I0419 03:01:25.672645 140363059820416 interactiveshell.py:2882] Step 13600 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001722 I0419 03:01:28.025345 140363059820416 interactiveshell.py:2882] Step 13650 | Loss: 0.0003 | Spent: 2.4 secs | LR: 0.001718 I0419 03:01:30.365940 140363059820416 interactiveshell.py:2882] Step 13700 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001715 I0419 03:01:32.701982 140363059820416 interactiveshell.py:2882] Step 13750 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001711 I0419 03:01:34.939349 140363059820416 interactiveshell.py:2882] Step 13800 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001708 I0419 03:01:37.098617 140363059820416 interactiveshell.py:2882] Step 13850 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001704 I0419 03:01:39.247241 140363059820416 interactiveshell.py:2882] Step 13900 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001701 I0419 03:01:41.409868 140363059820416 interactiveshell.py:2882] Step 13950 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001697 I0419 03:01:43.575518 140363059820416 interactiveshell.py:2882] Step 14000 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001694 I0419 03:01:45.751561 140363059820416 interactiveshell.py:2882] Step 14050 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001691 I0419 03:01:47.911783 140363059820416 interactiveshell.py:2882] Step 14100 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001687 I0419 03:01:50.096114 140363059820416 interactiveshell.py:2882] Step 14150 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001684 I0419 03:01:52.261914 140363059820416 interactiveshell.py:2882] Step 14200 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001680 I0419 03:01:54.425772 140363059820416 interactiveshell.py:2882] Step 14250 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001677 I0419 03:01:56.589558 140363059820416 interactiveshell.py:2882] Step 14300 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001673 I0419 03:01:58.746105 140363059820416 interactiveshell.py:2882] Step 14350 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001670
Reading ../data/wn18/test.txt
I0419 03:02:01.929275 140363059820416 interactiveshell.py:2882] MRR: 0.810| [email protected]: 0.949 | [email protected]: 0.926 | [email protected]: 0.692 I0419 03:02:01.934513 140363059820416 interactiveshell.py:2882] Best MRR: 0.810
Reading ../data/wn18/train.txt
I0419 03:02:27.826611 140363059820416 interactiveshell.py:2882] Step 14400 | Loss: 0.0002 | Spent: 29.1 secs | LR: 0.001667 I0419 03:02:29.987325 140363059820416 interactiveshell.py:2882] Step 14450 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001663 I0419 03:02:32.123877 140363059820416 interactiveshell.py:2882] Step 14500 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001660 I0419 03:02:34.467497 140363059820416 interactiveshell.py:2882] Step 14550 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001656 I0419 03:02:36.842844 140363059820416 interactiveshell.py:2882] Step 14600 | Loss: 0.0003 | Spent: 2.4 secs | LR: 0.001653 I0419 03:02:39.261272 140363059820416 interactiveshell.py:2882] Step 14650 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001650 I0419 03:02:41.688321 140363059820416 interactiveshell.py:2882] Step 14700 | Loss: 0.0004 | Spent: 2.4 secs | LR: 0.001646 I0419 03:02:43.982613 140363059820416 interactiveshell.py:2882] Step 14750 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001643 I0419 03:02:46.324828 140363059820416 interactiveshell.py:2882] Step 14800 | Loss: 0.0004 | Spent: 2.3 secs | LR: 0.001640 I0419 03:02:48.680406 140363059820416 interactiveshell.py:2882] Step 14850 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001636 I0419 03:02:51.023689 140363059820416 interactiveshell.py:2882] Step 14900 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001633 I0419 03:02:53.366230 140363059820416 interactiveshell.py:2882] Step 14950 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001630 I0419 03:02:55.575829 140363059820416 interactiveshell.py:2882] Step 15000 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001626 I0419 03:02:57.734235 140363059820416 interactiveshell.py:2882] Step 15050 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001623 I0419 03:02:59.907406 140363059820416 interactiveshell.py:2882] Step 15100 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001620 I0419 03:03:02.078325 140363059820416 interactiveshell.py:2882] Step 15150 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001616 I0419 03:03:04.244468 140363059820416 interactiveshell.py:2882] Step 15200 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001613 I0419 03:03:06.389238 140363059820416 interactiveshell.py:2882] Step 15250 | Loss: 0.0003 | Spent: 2.1 secs | LR: 0.001610 I0419 03:03:08.557773 140363059820416 interactiveshell.py:2882] Step 15300 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001606 I0419 03:03:10.724197 140363059820416 interactiveshell.py:2882] Step 15350 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001603 I0419 03:03:12.891543 140363059820416 interactiveshell.py:2882] Step 15400 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001600 I0419 03:03:15.056863 140363059820416 interactiveshell.py:2882] Step 15450 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001597
Reading ../data/wn18/test.txt
I0419 03:03:18.524386 140363059820416 interactiveshell.py:2882] MRR: 0.807| [email protected]: 0.949 | [email protected]: 0.926 | [email protected]: 0.688 I0419 03:03:18.529479 140363059820416 interactiveshell.py:2882] Best MRR: 0.810
Reading ../data/wn18/train.txt
I0419 03:03:44.047668 140363059820416 interactiveshell.py:2882] Step 15500 | Loss: 0.0003 | Spent: 29.0 secs | LR: 0.001593 I0419 03:03:46.213158 140363059820416 interactiveshell.py:2882] Step 15550 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001590 I0419 03:03:48.367478 140363059820416 interactiveshell.py:2882] Step 15600 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001587 I0419 03:03:50.509772 140363059820416 interactiveshell.py:2882] Step 15650 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001584 I0419 03:03:52.662824 140363059820416 interactiveshell.py:2882] Step 15700 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001580 I0419 03:03:54.817422 140363059820416 interactiveshell.py:2882] Step 15750 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001577 I0419 03:03:56.971871 140363059820416 interactiveshell.py:2882] Step 15800 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001574 I0419 03:03:59.146476 140363059820416 interactiveshell.py:2882] Step 15850 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001571 I0419 03:04:01.300762 140363059820416 interactiveshell.py:2882] Step 15900 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001568 I0419 03:04:03.464588 140363059820416 interactiveshell.py:2882] Step 15950 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001564 I0419 03:04:05.743123 140363059820416 interactiveshell.py:2882] Step 16000 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001561 I0419 03:04:08.086133 140363059820416 interactiveshell.py:2882] Step 16050 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001558 I0419 03:04:10.436881 140363059820416 interactiveshell.py:2882] Step 16100 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001555 I0419 03:04:12.798673 140363059820416 interactiveshell.py:2882] Step 16150 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001552 I0419 03:04:15.072909 140363059820416 interactiveshell.py:2882] Step 16200 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001549 I0419 03:04:17.239684 140363059820416 interactiveshell.py:2882] Step 16250 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001545 I0419 03:04:19.390938 140363059820416 interactiveshell.py:2882] Step 16300 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001542 I0419 03:04:21.553794 140363059820416 interactiveshell.py:2882] Step 16350 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001539 I0419 03:04:23.727125 140363059820416 interactiveshell.py:2882] Step 16400 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001536 I0419 03:04:25.909080 140363059820416 interactiveshell.py:2882] Step 16450 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001533 I0419 03:04:28.096504 140363059820416 interactiveshell.py:2882] Step 16500 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001530 I0419 03:04:30.283957 140363059820416 interactiveshell.py:2882] Step 16550 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001527
Reading ../data/wn18/test.txt
I0419 03:04:34.009741 140363059820416 interactiveshell.py:2882] MRR: 0.804| [email protected]: 0.947 | [email protected]: 0.922 | [email protected]: 0.683 I0419 03:04:34.013939 140363059820416 interactiveshell.py:2882] Best MRR: 0.810
Reading ../data/wn18/train.txt
I0419 03:05:00.217474 140363059820416 interactiveshell.py:2882] Step 16600 | Loss: 0.0002 | Spent: 29.9 secs | LR: 0.001523 I0419 03:05:02.405017 140363059820416 interactiveshell.py:2882] Step 16650 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001520 I0419 03:05:04.558930 140363059820416 interactiveshell.py:2882] Step 16700 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001517 I0419 03:05:06.707412 140363059820416 interactiveshell.py:2882] Step 16750 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001514 I0419 03:05:08.867673 140363059820416 interactiveshell.py:2882] Step 16800 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001511 I0419 03:05:11.033065 140363059820416 interactiveshell.py:2882] Step 16850 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001508 I0419 03:05:13.199657 140363059820416 interactiveshell.py:2882] Step 16900 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001505 I0419 03:05:15.355546 140363059820416 interactiveshell.py:2882] Step 16950 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001502 I0419 03:05:17.528534 140363059820416 interactiveshell.py:2882] Step 17000 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001499 I0419 03:05:19.704082 140363059820416 interactiveshell.py:2882] Step 17050 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001496 I0419 03:05:21.858234 140363059820416 interactiveshell.py:2882] Step 17100 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001493 I0419 03:05:24.026916 140363059820416 interactiveshell.py:2882] Step 17150 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001490 I0419 03:05:26.317033 140363059820416 interactiveshell.py:2882] Step 17200 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001487 I0419 03:05:28.669998 140363059820416 interactiveshell.py:2882] Step 17250 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001484 I0419 03:05:31.026062 140363059820416 interactiveshell.py:2882] Step 17300 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001481 I0419 03:05:33.375689 140363059820416 interactiveshell.py:2882] Step 17350 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001477 I0419 03:05:35.644837 140363059820416 interactiveshell.py:2882] Step 17400 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001474 I0419 03:05:37.824846 140363059820416 interactiveshell.py:2882] Step 17450 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001471 I0419 03:05:39.990617 140363059820416 interactiveshell.py:2882] Step 17500 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001468 I0419 03:05:42.152621 140363059820416 interactiveshell.py:2882] Step 17550 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001465 I0419 03:05:44.307726 140363059820416 interactiveshell.py:2882] Step 17600 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001462 I0419 03:05:46.471768 140363059820416 interactiveshell.py:2882] Step 17650 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001460
Reading ../data/wn18/test.txt
I0419 03:05:50.392532 140363059820416 interactiveshell.py:2882] MRR: 0.804| [email protected]: 0.947 | [email protected]: 0.924 | [email protected]: 0.682 I0419 03:05:50.394960 140363059820416 interactiveshell.py:2882] Best MRR: 0.810
Reading ../data/wn18/train.txt
I0419 03:06:15.079569 140363059820416 interactiveshell.py:2882] Step 17700 | Loss: 0.0004 | Spent: 28.6 secs | LR: 0.001457 I0419 03:06:17.265018 140363059820416 interactiveshell.py:2882] Step 17750 | Loss: 0.0001 | Spent: 2.2 secs | LR: 0.001454 I0419 03:06:19.413024 140363059820416 interactiveshell.py:2882] Step 17800 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001451 I0419 03:06:21.564203 140363059820416 interactiveshell.py:2882] Step 17850 | Loss: 0.0002 | Spent: 2.1 secs | LR: 0.001448 I0419 03:06:23.718847 140363059820416 interactiveshell.py:2882] Step 17900 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001445 I0419 03:06:25.896656 140363059820416 interactiveshell.py:2882] Step 17950 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001442 I0419 03:06:28.048960 140363059820416 interactiveshell.py:2882] Step 18000 | Loss: 0.0001 | Spent: 2.2 secs | LR: 0.001439 I0419 03:06:30.204495 140363059820416 interactiveshell.py:2882] Step 18050 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001436 I0419 03:06:32.364904 140363059820416 interactiveshell.py:2882] Step 18100 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001433 I0419 03:06:34.538787 140363059820416 interactiveshell.py:2882] Step 18150 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001430 I0419 03:06:36.710727 140363059820416 interactiveshell.py:2882] Step 18200 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001427 I0419 03:06:38.908045 140363059820416 interactiveshell.py:2882] Step 18250 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001424 I0419 03:06:41.068333 140363059820416 interactiveshell.py:2882] Step 18300 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001421 I0419 03:06:43.228185 140363059820416 interactiveshell.py:2882] Step 18350 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001418 I0419 03:06:45.435175 140363059820416 interactiveshell.py:2882] Step 18400 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001416 I0419 03:06:47.800668 140363059820416 interactiveshell.py:2882] Step 18450 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001413 I0419 03:06:50.150009 140363059820416 interactiveshell.py:2882] Step 18500 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001410 I0419 03:06:52.508491 140363059820416 interactiveshell.py:2882] Step 18550 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001407 I0419 03:06:54.856913 140363059820416 interactiveshell.py:2882] Step 18600 | Loss: 0.0003 | Spent: 2.3 secs | LR: 0.001404 I0419 03:06:57.049504 140363059820416 interactiveshell.py:2882] Step 18650 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001401 I0419 03:06:59.213945 140363059820416 interactiveshell.py:2882] Step 18700 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001398 I0419 03:07:01.378394 140363059820416 interactiveshell.py:2882] Step 18750 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001395 I0419 03:07:03.549765 140363059820416 interactiveshell.py:2882] Step 18800 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001393
Reading ../data/wn18/test.txt
I0419 03:07:05.613154 140363059820416 interactiveshell.py:2882] MRR: 0.799| [email protected]: 0.948 | [email protected]: 0.923 | [email protected]: 0.671 I0419 03:07:05.616983 140363059820416 interactiveshell.py:2882] Best MRR: 0.810
Reading ../data/wn18/train.txt
I0419 03:07:32.352391 140363059820416 interactiveshell.py:2882] Step 18850 | Loss: 0.0002 | Spent: 28.8 secs | LR: 0.001390 I0419 03:07:34.530455 140363059820416 interactiveshell.py:2882] Step 18900 | Loss: 0.0001 | Spent: 2.2 secs | LR: 0.001387 I0419 03:07:36.684872 140363059820416 interactiveshell.py:2882] Step 18950 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001384 I0419 03:07:38.839009 140363059820416 interactiveshell.py:2882] Step 19000 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001381 I0419 03:07:41.010899 140363059820416 interactiveshell.py:2882] Step 19050 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001378 I0419 03:07:43.196222 140363059820416 interactiveshell.py:2882] Step 19100 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001376 I0419 03:07:45.600788 140363059820416 interactiveshell.py:2882] Step 19150 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001373 I0419 03:07:48.042237 140363059820416 interactiveshell.py:2882] Step 19200 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001370 I0419 03:07:50.468606 140363059820416 interactiveshell.py:2882] Step 19250 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001367 I0419 03:07:52.897455 140363059820416 interactiveshell.py:2882] Step 19300 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001364 I0419 03:07:55.113472 140363059820416 interactiveshell.py:2882] Step 19350 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001362 I0419 03:07:57.293524 140363059820416 interactiveshell.py:2882] Step 19400 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001359 I0419 03:07:59.471431 140363059820416 interactiveshell.py:2882] Step 19450 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001356 I0419 03:08:01.640886 140363059820416 interactiveshell.py:2882] Step 19500 | Loss: 0.0010 | Spent: 2.2 secs | LR: 0.001353 I0419 03:08:03.825306 140363059820416 interactiveshell.py:2882] Step 19550 | Loss: 0.0004 | Spent: 2.2 secs | LR: 0.001351 I0419 03:08:06.067859 140363059820416 interactiveshell.py:2882] Step 19600 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001348 I0419 03:08:08.435000 140363059820416 interactiveshell.py:2882] Step 19650 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001345 I0419 03:08:10.797613 140363059820416 interactiveshell.py:2882] Step 19700 | Loss: 0.0004 | Spent: 2.4 secs | LR: 0.001342 I0419 03:08:13.152040 140363059820416 interactiveshell.py:2882] Step 19750 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001340 I0419 03:08:15.506778 140363059820416 interactiveshell.py:2882] Step 19800 | Loss: 0.0002 | Spent: 2.4 secs | LR: 0.001337 I0419 03:08:17.673060 140363059820416 interactiveshell.py:2882] Step 19850 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001334 I0419 03:08:19.851164 140363059820416 interactiveshell.py:2882] Step 19900 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001331
Reading ../data/wn18/test.txt
I0419 03:08:22.219443 140363059820416 interactiveshell.py:2882] MRR: 0.798| [email protected]: 0.949 | [email protected]: 0.926 | [email protected]: 0.668 I0419 03:08:22.224124 140363059820416 interactiveshell.py:2882] Best MRR: 0.810
Reading ../data/wn18/train.txt
I0419 03:08:49.069505 140363059820416 interactiveshell.py:2882] Step 19950 | Loss: 0.0002 | Spent: 29.2 secs | LR: 0.001329 I0419 03:08:51.235039 140363059820416 interactiveshell.py:2882] Step 20000 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001326 I0419 03:08:53.394015 140363059820416 interactiveshell.py:2882] Step 20050 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001323 I0419 03:08:55.569463 140363059820416 interactiveshell.py:2882] Step 20100 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001321 I0419 03:08:57.789127 140363059820416 interactiveshell.py:2882] Step 20150 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001318 I0419 03:08:59.960206 140363059820416 interactiveshell.py:2882] Step 20200 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001315 I0419 03:09:02.139737 140363059820416 interactiveshell.py:2882] Step 20250 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001313 I0419 03:09:04.325982 140363059820416 interactiveshell.py:2882] Step 20300 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001310 I0419 03:09:06.510616 140363059820416 interactiveshell.py:2882] Step 20350 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001307 I0419 03:09:08.681034 140363059820416 interactiveshell.py:2882] Step 20400 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001305 I0419 03:09:10.867643 140363059820416 interactiveshell.py:2882] Step 20450 | Loss: 0.0003 | Spent: 2.2 secs | LR: 0.001302 I0419 03:09:13.046737 140363059820416 interactiveshell.py:2882] Step 20500 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001299 I0419 03:09:15.234053 140363059820416 interactiveshell.py:2882] Step 20550 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001297 I0419 03:09:17.418440 140363059820416 interactiveshell.py:2882] Step 20600 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001294 I0419 03:09:19.615988 140363059820416 interactiveshell.py:2882] Step 20650 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001291 I0419 03:09:21.796715 140363059820416 interactiveshell.py:2882] Step 20700 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001289 I0419 03:09:23.975501 140363059820416 interactiveshell.py:2882] Step 20750 | Loss: 0.0001 | Spent: 2.2 secs | LR: 0.001286 I0419 03:09:26.199164 140363059820416 interactiveshell.py:2882] Step 20800 | Loss: 0.0002 | Spent: 2.2 secs | LR: 0.001283 I0419 03:09:28.545131 140363059820416 interactiveshell.py:2882] Step 20850 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001281 I0419 03:09:30.888232 140363059820416 interactiveshell.py:2882] Step 20900 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001278 I0419 03:09:33.229095 140363059820416 interactiveshell.py:2882] Step 20950 | Loss: 0.0002 | Spent: 2.3 secs | LR: 0.001276 I0419 03:09:35.584407 140363059820416 interactiveshell.py:2882] Step 21000 | Loss: 0.0001 | Spent: 2.4 secs | LR: 0.001273
Reading ../data/wn18/test.txt
I0419 03:09:38.220379 140363059820416 interactiveshell.py:2882] MRR: 0.795| [email protected]: 0.947 | [email protected]: 0.923 | [email protected]: 0.665 I0419 03:09:38.225623 140363059820416 interactiveshell.py:2882] Best MRR: 0.810 I0419 03:09:38.226562 140363059820416 interactiveshell.py:2882] MRR not improved over 3 epochs, Early Stop