word2vecの実装に、推論を使用する。前の章ではカウントベースの手法を使用したが、今回は推論ベースの手法を使用する。
カウントベースの手法を使うと、コーパスで扱う語彙数が非常に巨大になり、例えば100万×100万の巨大な行列を作る必要が発生してしまう。 このような巨大な行列に対してSVDを行うことは、現実的ではない。
これに対して、推論ベースの手法では、ニューラルねとワークを使う場合は、ミニバッチを使って学習を行う。
推論ベースの手法では、例えば"You ? goodbay and I say hello."という文字列に対して、?に何が入るかを推論することになる。
単語のIDから、one-hotへと変換する。
このベクトルに行列を掛け合わせることで、ニューラルネットワークを実現する。
ニューラルネットワークに対してword2vecを組み込むために、"continuous bag-of-words : CBOW"と呼ばれるモデルを使用すrる。
CBOWは、
import sys
sys.path.append('..')
import numpy as np
from common.layers import MatMul
c0 = np.array([[1, 0, 0, 0, 0, 0, 0]])
c1 = np.array([[0, 0, 1, 0, 0, 0, 0]])
W_in = np.random.randn(7, 3)
W_out = np.random.randn(3, 7)
in_layer0 = MatMul(W_in)
in_layer1 = MatMul(W_in)
out_layer = MatMul(W_out)
h0 = in_layer0.forward(c0)
h1 = in_layer1.forward(c1)
h = 0.5 * (h0 + h1)
s = out_layer.forward(h)
print(s)
[[ 5.32638403e-04 -1.25152629e+00 2.11653615e-01 -1.00560486e+00 -2.98510116e-02 -5.22961401e-01 4.43117271e-01]]
まずは、サンプルの文章からコーパスを作成する。id_to_wordは辞書に相当する。
import sys
sys.path.append('..')
from common.util import preprocess
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
print(corpus)
print(id_to_word)
[0 1 2 3 4 1 5 6] {0: 'you', 1: 'say', 2: 'goodbye', 3: 'and', 4: 'i', 5: 'hello', 6: '.'}
次に、作成したcorpusを元にonehotの配列を作成する。
下記のtarget
は推論する対象の単語(のインデックス)、contexts
は推論する対象に対するコンテキスト(つまり隣接する単語)。
import sys
sys.path.append('..')
from common.util import preprocess, create_contexts_target, convert_one_hot
def create_contexts_target(corpus, window_size=1):
target = corpus[window_size:-window_size]
contexts = []
for idx in range(window_size, len(corpus)-window_size):
cs = []
for t in range(-window_size, window_size+1):
if t == 0:
continue
cs.append(corpus[idx+t])
contexts.append(cs)
return np.array(contexts), np.array(target)
contexts, target = create_contexts_target(corpus, window_size=1)
vocab_size = len(word_to_id)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
print(target)
print(contexts)
[[0 1 0 0 0 0 0] [0 0 1 0 0 0 0] [0 0 0 1 0 0 0] [0 0 0 0 1 0 0] [0 1 0 0 0 0 0] [0 0 0 0 0 1 0]] [[[1 0 0 0 0 0 0] [0 0 1 0 0 0 0]] [[0 1 0 0 0 0 0] [0 0 0 1 0 0 0]] [[0 0 1 0 0 0 0] [0 0 0 0 1 0 0]] [[0 0 0 1 0 0 0] [0 1 0 0 0 0 0]] [[0 0 0 0 1 0 0] [0 0 0 0 0 1 0]] [[0 1 0 0 0 0 0] [0 0 0 0 0 0 1]]]
ニューラルネットワークの実装。
layer0
とlayer1
に対して重みを掛け合わせ、最後に平均を取るのが順方向の処理。
import sys
sys.path.append('..')
import numpy as np
from common.layers import MatMul, SoftmaxWithLoss
class SimpleCBOW:
def __init__(self, vocab_size, hidden_size):
V, H = vocab_size, hidden_size
# 重みの初期化
W_in = 0.01 * np.random.randn(V, H).astype('f')
W_out = 0.01 * np.random.randn(H, V).astype('f')
# レイヤの生成
self.in_layer0 = MatMul(W_in)
self.in_layer1 = MatMul(W_in)
self.out_layer = MatMul(W_out)
self.loss_layer = SoftmaxWithLoss()
# すべての重みと勾配をリストにまとめる
layers = [self.in_layer0, self.in_layer1, self.out_layer]
self.params, self.grads = [], []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
# メンバ変数に単語の分散表現を設定
self.word_vecs = W_in
def forward(self, contexts, target):
h0 = self.in_layer0.forward(contexts[:, 0])
h1 = self.in_layer1.forward(contexts[:, 1])
h = (h0 + h1) * 0.5
score = self.out_layer.forward(h)
loss = self.loss_layer.forward(score, target)
return loss
def backward(self, dout=1):
ds = self.loss_layer.backward(dout)
da = self.out_layer.backward(ds)
da *= 0.5
self.in_layer1.backward(da)
self.in_layer0.backward(da)
return None
実行結果。誤答率が下がっていることが分かる。
# coding: utf-8
import sys
sys.path.append('..') # 親ディレクトリのファイルをインポートするための設定
from common.trainer import Trainer
from common.optimizer import Adam
from simple_cbow import SimpleCBOW
from common.util import preprocess, create_contexts_target, convert_one_hot
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
| epoch 1 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 2 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 3 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 4 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 5 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 6 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 7 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 8 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 9 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 10 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 11 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 12 | iter 1 / 2 | time 0[s] | loss 1.95 | epoch 13 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 14 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 15 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 16 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 17 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 18 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 19 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 20 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 21 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 22 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 23 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 24 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 25 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 26 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 27 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 28 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 29 | iter 1 / 2 | time 0[s] | loss 1.94 | epoch 30 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 31 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 32 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 33 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 34 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 35 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 36 | iter 1 / 2 | time 0[s] | loss 1.93 | epoch 37 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 38 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 39 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 40 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 41 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 42 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 43 | iter 1 / 2 | time 0[s] | loss 1.92 | epoch 44 | iter 1 / 2 | time 0[s] | loss 1.91 | epoch 45 | iter 1 / 2 | time 0[s] | loss 1.91 | epoch 46 | iter 1 / 2 | time 0[s] | loss 1.91 | epoch 47 | iter 1 / 2 | time 0[s] | loss 1.91 | epoch 48 | iter 1 / 2 | time 0[s] | loss 1.90 | epoch 49 | iter 1 / 2 | time 0[s] | loss 1.90 | epoch 50 | iter 1 / 2 | time 0[s] | loss 1.90 | epoch 51 | iter 1 / 2 | time 0[s] | loss 1.90 | epoch 52 | iter 1 / 2 | time 0[s] | loss 1.90 | epoch 53 | iter 1 / 2 | time 0[s] | loss 1.89 | epoch 54 | iter 1 / 2 | time 0[s] | loss 1.89 | epoch 55 | iter 1 / 2 | time 0[s] | loss 1.89 | epoch 56 | iter 1 / 2 | time 0[s] | loss 1.88 | epoch 57 | iter 1 / 2 | time 0[s] | loss 1.89 | epoch 58 | iter 1 / 2 | time 0[s] | loss 1.88 | epoch 59 | iter 1 / 2 | time 0[s] | loss 1.88 | epoch 60 | iter 1 / 2 | time 0[s] | loss 1.87 | epoch 61 | iter 1 / 2 | time 0[s] | loss 1.87 | epoch 62 | iter 1 / 2 | time 0[s] | loss 1.87 | epoch 63 | iter 1 / 2 | time 0[s] | loss 1.86 | epoch 64 | iter 1 / 2 | time 0[s] | loss 1.86 | epoch 65 | iter 1 / 2 | time 0[s] | loss 1.86 | epoch 66 | iter 1 / 2 | time 0[s] | loss 1.86 | epoch 67 | iter 1 / 2 | time 0[s] | loss 1.85 | epoch 68 | iter 1 / 2 | time 0[s] | loss 1.84 | epoch 69 | iter 1 / 2 | time 0[s] | loss 1.85 | epoch 70 | iter 1 / 2 | time 0[s] | loss 1.84 | epoch 71 | iter 1 / 2 | time 0[s] | loss 1.83 | epoch 72 | iter 1 / 2 | time 0[s] | loss 1.84 | epoch 73 | iter 1 / 2 | time 0[s] | loss 1.83 | epoch 74 | iter 1 / 2 | time 0[s] | loss 1.83 | epoch 75 | iter 1 / 2 | time 0[s] | loss 1.82 | epoch 76 | iter 1 / 2 | time 0[s] | loss 1.83 | epoch 77 | iter 1 / 2 | time 0[s] | loss 1.81 | epoch 78 | iter 1 / 2 | time 0[s] | loss 1.82 | epoch 79 | iter 1 / 2 | time 0[s] | loss 1.80 | epoch 80 | iter 1 / 2 | time 0[s] | loss 1.81 | epoch 81 | iter 1 / 2 | time 0[s] | loss 1.80 | epoch 82 | iter 1 / 2 | time 0[s] | loss 1.80 | epoch 83 | iter 1 / 2 | time 0[s] | loss 1.79 | epoch 84 | iter 1 / 2 | 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0[s] | loss 1.60 | epoch 126 | iter 1 / 2 | time 0[s] | loss 1.59 | epoch 127 | iter 1 / 2 | time 0[s] | loss 1.59 | epoch 128 | iter 1 / 2 | time 0[s] | loss 1.59 | epoch 129 | iter 1 / 2 | time 0[s] | loss 1.59 | epoch 130 | iter 1 / 2 | time 0[s] | loss 1.57 | epoch 131 | iter 1 / 2 | time 0[s] | loss 1.60 | epoch 132 | iter 1 / 2 | time 0[s] | loss 1.54 | epoch 133 | iter 1 / 2 | time 0[s] | loss 1.60 | epoch 134 | iter 1 / 2 | time 0[s] | loss 1.56 | epoch 135 | iter 1 / 2 | time 0[s] | loss 1.56 | epoch 136 | iter 1 / 2 | time 0[s] | loss 1.52 | epoch 137 | iter 1 / 2 | time 0[s] | loss 1.55 | epoch 138 | iter 1 / 2 | time 0[s] | loss 1.53 | epoch 139 | iter 1 / 2 | time 0[s] | loss 1.56 | epoch 140 | iter 1 / 2 | time 0[s] | loss 1.55 | epoch 141 | iter 1 / 2 | time 0[s] | loss 1.50 | epoch 142 | iter 1 / 2 | time 0[s] | loss 1.55 | epoch 143 | iter 1 / 2 | time 0[s] | loss 1.51 | epoch 144 | iter 1 / 2 | time 0[s] | loss 1.53 | epoch 145 | iter 1 / 2 | time 0[s] | loss 1.51 | epoch 146 | iter 1 / 2 | time 0[s] | loss 1.48 | epoch 147 | iter 1 / 2 | time 0[s] | loss 1.50 | epoch 148 | iter 1 / 2 | time 0[s] | loss 1.48 | epoch 149 | iter 1 / 2 | time 0[s] | loss 1.49 | epoch 150 | iter 1 / 2 | time 0[s] | loss 1.50 | epoch 151 | iter 1 / 2 | time 0[s] | loss 1.51 | epoch 152 | iter 1 / 2 | time 0[s] | loss 1.42 | epoch 153 | iter 1 / 2 | time 0[s] | loss 1.49 | epoch 154 | iter 1 / 2 | time 0[s] | loss 1.49 | epoch 155 | iter 1 / 2 | time 0[s] | loss 1.43 | epoch 156 | iter 1 / 2 | time 0[s] | loss 1.49 | epoch 157 | iter 1 / 2 | time 0[s] | loss 1.46 | epoch 158 | iter 1 / 2 | time 0[s] | loss 1.44 | epoch 159 | iter 1 / 2 | time 0[s] | loss 1.44 | epoch 160 | iter 1 / 2 | time 0[s] | loss 1.42 | epoch 161 | iter 1 / 2 | time 0[s] | loss 1.45 | epoch 162 | iter 1 / 2 | time 0[s] | loss 1.40 | epoch 163 | iter 1 / 2 | time 0[s] | loss 1.46 | epoch 164 | iter 1 / 2 | time 0[s] | loss 1.37 | epoch 165 | iter 1 / 2 | time 0[s] | loss 1.44 | epoch 166 | iter 1 / 2 | time 0[s] | loss 1.41 | epoch 167 | iter 1 / 2 | time 0[s] | loss 1.44 | epoch 168 | iter 1 / 2 | time 0[s] | loss 1.35 | epoch 169 | iter 1 / 2 | time 0[s] | loss 1.44 | epoch 170 | iter 1 / 2 | time 0[s] | loss 1.41 | epoch 171 | iter 1 / 2 | time 0[s] | loss 1.36 | epoch 172 | iter 1 / 2 | time 0[s] | loss 1.45 | epoch 173 | iter 1 / 2 | time 0[s] | loss 1.31 | epoch 174 | iter 1 / 2 | time 0[s] | loss 1.42 | epoch 175 | iter 1 / 2 | time 0[s] | loss 1.38 | epoch 176 | iter 1 / 2 | time 0[s] | loss 1.36 | epoch 177 | iter 1 / 2 | time 0[s] | loss 1.39 | epoch 178 | iter 1 / 2 | time 0[s] | loss 1.28 | epoch 179 | iter 1 / 2 | time 0[s] | loss 1.35 | epoch 180 | iter 1 / 2 | time 0[s] | loss 1.41 | epoch 181 | iter 1 / 2 | time 0[s] | loss 1.30 | epoch 182 | iter 1 / 2 | time 0[s] | loss 1.34 | epoch 183 | iter 1 / 2 | time 0[s] | loss 1.32 | epoch 184 | iter 1 / 2 | time 0[s] | loss 1.40 | epoch 185 | iter 1 / 2 | time 0[s] | loss 1.24 | epoch 186 | iter 1 / 2 | time 0[s] | loss 1.37 | epoch 187 | iter 1 / 2 | time 0[s] | loss 1.36 | epoch 188 | iter 1 / 2 | time 0[s] | loss 1.30 | epoch 189 | iter 1 / 2 | time 0[s] | loss 1.28 | epoch 190 | iter 1 / 2 | time 0[s] | loss 1.36 | epoch 191 | iter 1 / 2 | time 0[s] | loss 1.23 | epoch 192 | iter 1 / 2 | time 0[s] | loss 1.36 | epoch 193 | iter 1 / 2 | time 0[s] | loss 1.28 | epoch 194 | iter 1 / 2 | time 0[s] | loss 1.31 | epoch 195 | iter 1 / 2 | time 0[s] | loss 1.29 | epoch 196 | iter 1 / 2 | time 0[s] | loss 1.25 | epoch 197 | iter 1 / 2 | time 0[s] | loss 1.30 | epoch 198 | iter 1 / 2 | time 0[s] | loss 1.29 | epoch 199 | iter 1 / 2 | time 0[s] | loss 1.24 | epoch 200 | iter 1 / 2 | time 0[s] | loss 1.29 | epoch 201 | iter 1 / 2 | time 0[s] | loss 1.24 | epoch 202 | iter 1 / 2 | time 0[s] | loss 1.24 | epoch 203 | iter 1 / 2 | time 0[s] | loss 1.34 | epoch 204 | iter 1 / 2 | time 0[s] | loss 1.25 | epoch 205 | iter 1 / 2 | time 0[s] | loss 1.25 | epoch 206 | iter 1 / 2 | time 0[s] | loss 1.20 | epoch 207 | 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0[s] | loss 1.15 | epoch 228 | iter 1 / 2 | time 0[s] | loss 1.13 | epoch 229 | iter 1 / 2 | time 0[s] | loss 1.11 | epoch 230 | iter 1 / 2 | time 0[s] | loss 1.23 | epoch 231 | iter 1 / 2 | time 0[s] | loss 1.16 | epoch 232 | iter 1 / 2 | time 0[s] | loss 1.10 | epoch 233 | iter 1 / 2 | time 0[s] | loss 1.20 | epoch 234 | iter 1 / 2 | time 0[s] | loss 1.15 | epoch 235 | iter 1 / 2 | time 0[s] | loss 1.14 | epoch 236 | iter 1 / 2 | time 0[s] | loss 1.14 | epoch 237 | iter 1 / 2 | time 0[s] | loss 1.13 | epoch 238 | iter 1 / 2 | time 0[s] | loss 1.11 | epoch 239 | iter 1 / 2 | time 0[s] | loss 1.16 | epoch 240 | iter 1 / 2 | time 0[s] | loss 1.07 | epoch 241 | iter 1 / 2 | time 0[s] | loss 1.22 | epoch 242 | iter 1 / 2 | time 0[s] | loss 1.08 | epoch 243 | iter 1 / 2 | time 0[s] | loss 1.04 | epoch 244 | iter 1 / 2 | time 0[s] | loss 1.09 | epoch 245 | iter 1 / 2 | time 0[s] | loss 1.25 | epoch 246 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 247 | iter 1 / 2 | time 0[s] | loss 1.12 | epoch 248 | iter 1 / 2 | time 0[s] | loss 1.05 | epoch 249 | iter 1 / 2 | time 0[s] | loss 1.16 | epoch 250 | iter 1 / 2 | time 0[s] | loss 1.08 | epoch 251 | iter 1 / 2 | time 0[s] | loss 1.09 | epoch 252 | iter 1 / 2 | time 0[s] | loss 1.01 | epoch 253 | iter 1 / 2 | time 0[s] | loss 1.10 | epoch 254 | iter 1 / 2 | time 0[s] | loss 1.14 | epoch 255 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 256 | iter 1 / 2 | time 0[s] | loss 1.19 | epoch 257 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 258 | iter 1 / 2 | time 0[s] | loss 1.07 | epoch 259 | iter 1 / 2 | time 0[s] | loss 1.05 | epoch 260 | iter 1 / 2 | time 0[s] | loss 1.00 | epoch 261 | iter 1 / 2 | time 0[s] | loss 1.19 | epoch 262 | iter 1 / 2 | time 0[s] | loss 0.94 | epoch 263 | iter 1 / 2 | time 0[s] | loss 1.11 | epoch 264 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 265 | iter 1 / 2 | time 0[s] | loss 1.08 | epoch 266 | iter 1 / 2 | time 0[s] | loss 0.96 | epoch 267 | iter 1 / 2 | time 0[s] | loss 1.11 | epoch 268 | iter 1 / 2 | time 0[s] | loss 1.12 | epoch 269 | iter 1 / 2 | time 0[s] | loss 0.95 | epoch 270 | iter 1 / 2 | time 0[s] | loss 1.01 | epoch 271 | iter 1 / 2 | time 0[s] | loss 0.96 | epoch 272 | iter 1 / 2 | time 0[s] | loss 1.19 | epoch 273 | iter 1 / 2 | time 0[s] | loss 0.94 | epoch 274 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 275 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 276 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 277 | iter 1 / 2 | time 0[s] | loss 0.99 | epoch 278 | iter 1 / 2 | time 0[s] | loss 1.01 | epoch 279 | iter 1 / 2 | time 0[s] | loss 1.04 | epoch 280 | iter 1 / 2 | time 0[s] | loss 1.01 | epoch 281 | iter 1 / 2 | time 0[s] | loss 1.00 | epoch 282 | iter 1 / 2 | time 0[s] | loss 1.00 | epoch 283 | iter 1 / 2 | time 0[s] | loss 0.97 | epoch 284 | iter 1 / 2 | time 0[s] | loss 1.10 | epoch 285 | iter 1 / 2 | time 0[s] | loss 0.89 | epoch 286 | iter 1 / 2 | time 0[s] | loss 0.99 | epoch 287 | iter 1 / 2 | time 0[s] | loss 1.02 | epoch 288 | iter 1 / 2 | time 0[s] | loss 0.90 | epoch 289 | iter 1 / 2 | time 0[s] | loss 1.04 | epoch 290 | iter 1 / 2 | time 0[s] | loss 1.07 | epoch 291 | iter 1 / 2 | time 0[s] | loss 0.89 | epoch 292 | iter 1 / 2 | time 0[s] | loss 0.90 | epoch 293 | iter 1 / 2 | time 0[s] | loss 1.14 | epoch 294 | iter 1 / 2 | time 0[s] | loss 0.89 | epoch 295 | iter 1 / 2 | time 0[s] | loss 0.97 | epoch 296 | iter 1 / 2 | time 0[s] | loss 0.98 | epoch 297 | iter 1 / 2 | time 0[s] | loss 0.97 | epoch 298 | iter 1 / 2 | time 0[s] | loss 0.85 | epoch 299 | iter 1 / 2 | time 0[s] | loss 0.98 | epoch 300 | iter 1 / 2 | time 0[s] | loss 1.04 | epoch 301 | iter 1 / 2 | time 0[s] | loss 1.01 | epoch 302 | iter 1 / 2 | time 0[s] | loss 0.81 | epoch 303 | iter 1 / 2 | time 0[s] | loss 0.95 | epoch 304 | iter 1 / 2 | time 0[s] | loss 1.04 | epoch 305 | iter 1 / 2 | time 0[s] | loss 0.92 | epoch 306 | iter 1 / 2 | time 0[s] | loss 0.94 | epoch 307 | iter 1 / 2 | time 0[s] | loss 0.87 | epoch 308 | iter 1 / 2 | time 0[s] | loss 0.94 | epoch 309 | 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0[s] | loss 0.80 | epoch 330 | iter 1 / 2 | time 0[s] | loss 1.05 | epoch 331 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 332 | iter 1 / 2 | time 0[s] | loss 0.88 | epoch 333 | iter 1 / 2 | time 0[s] | loss 0.88 | epoch 334 | iter 1 / 2 | time 0[s] | loss 0.88 | epoch 335 | iter 1 / 2 | time 0[s] | loss 0.96 | epoch 336 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 337 | iter 1 / 2 | time 0[s] | loss 0.96 | epoch 338 | iter 1 / 2 | time 0[s] | loss 0.78 | epoch 339 | iter 1 / 2 | time 0[s] | loss 0.89 | epoch 340 | iter 1 / 2 | time 0[s] | loss 0.96 | epoch 341 | iter 1 / 2 | time 0[s] | loss 0.69 | epoch 342 | iter 1 / 2 | time 0[s] | loss 0.95 | epoch 343 | iter 1 / 2 | time 0[s] | loss 0.94 | epoch 344 | iter 1 / 2 | time 0[s] | loss 0.77 | epoch 345 | iter 1 / 2 | time 0[s] | loss 0.94 | epoch 346 | iter 1 / 2 | time 0[s] | loss 0.77 | epoch 347 | iter 1 / 2 | time 0[s] | loss 0.78 | epoch 348 | iter 1 / 2 | time 0[s] | loss 0.95 | epoch 349 | iter 1 / 2 | time 0[s] | loss 0.84 | epoch 350 | iter 1 / 2 | time 0[s] | loss 0.76 | epoch 351 | iter 1 / 2 | time 0[s] | loss 1.03 | epoch 352 | iter 1 / 2 | time 0[s] | loss 0.74 | epoch 353 | iter 1 / 2 | time 0[s] | loss 0.77 | epoch 354 | iter 1 / 2 | time 0[s] | loss 0.84 | epoch 355 | iter 1 / 2 | time 0[s] | loss 1.02 | epoch 356 | iter 1 / 2 | time 0[s] | loss 0.73 | epoch 357 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 358 | iter 1 / 2 | time 0[s] | loss 0.87 | epoch 359 | iter 1 / 2 | time 0[s] | loss 0.73 | epoch 360 | iter 1 / 2 | time 0[s] | loss 0.90 | epoch 361 | iter 1 / 2 | time 0[s] | loss 0.91 | epoch 362 | iter 1 / 2 | time 0[s] | loss 0.65 | epoch 363 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 364 | iter 1 / 2 | time 0[s] | loss 0.92 | epoch 365 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 366 | iter 1 / 2 | time 0[s] | loss 0.74 | epoch 367 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 368 | iter 1 / 2 | time 0[s] | loss 0.91 | epoch 369 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 370 | iter 1 / 2 | time 0[s] | loss 0.81 | epoch 371 | iter 1 / 2 | time 0[s] | loss 0.83 | epoch 372 | iter 1 / 2 | time 0[s] | loss 0.76 | epoch 373 | iter 1 / 2 | time 0[s] | loss 0.73 | epoch 374 | iter 1 / 2 | time 0[s] | loss 0.88 | epoch 375 | iter 1 / 2 | time 0[s] | loss 0.85 | epoch 376 | iter 1 / 2 | time 0[s] | loss 0.78 | epoch 377 | iter 1 / 2 | time 0[s] | loss 0.72 | epoch 378 | iter 1 / 2 | time 0[s] | loss 0.90 | epoch 379 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 380 | iter 1 / 2 | time 0[s] | loss 0.77 | epoch 381 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 382 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 383 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 384 | iter 1 / 2 | time 0[s] | loss 0.71 | epoch 385 | iter 1 / 2 | time 0[s] | loss 0.89 | epoch 386 | iter 1 / 2 | time 0[s] | loss 0.83 | epoch 387 | iter 1 / 2 | time 0[s] | loss 0.73 | epoch 388 | iter 1 / 2 | time 0[s] | loss 0.86 | epoch 389 | iter 1 / 2 | time 0[s] | loss 0.78 | epoch 390 | iter 1 / 2 | time 0[s] | loss 0.70 | epoch 391 | iter 1 / 2 | time 0[s] | loss 0.75 | epoch 392 | iter 1 / 2 | time 0[s] | loss 0.88 | epoch 393 | iter 1 / 2 | time 0[s] | loss 0.70 | epoch 394 | iter 1 / 2 | time 0[s] | loss 0.84 | epoch 395 | iter 1 / 2 | time 0[s] | loss 0.69 | epoch 396 | iter 1 / 2 | time 0[s] | loss 0.74 | epoch 397 | iter 1 / 2 | time 0[s] | loss 0.66 | epoch 398 | iter 1 / 2 | time 0[s] | loss 0.87 | epoch 399 | iter 1 / 2 | time 0[s] | loss 0.66 | epoch 400 | iter 1 / 2 | time 0[s] | loss 0.86 | epoch 401 | iter 1 / 2 | time 0[s] | loss 0.83 | epoch 402 | iter 1 / 2 | time 0[s] | loss 0.68 | epoch 403 | iter 1 / 2 | time 0[s] | loss 0.79 | epoch 404 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 405 | iter 1 / 2 | time 0[s] | loss 0.89 | epoch 406 | iter 1 / 2 | time 0[s] | loss 0.72 | epoch 407 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 408 | iter 1 / 2 | time 0[s] | loss 0.71 | epoch 409 | iter 1 / 2 | time 0[s] | loss 0.82 | epoch 410 | iter 1 / 2 | time 0[s] | loss 0.64 | epoch 411 | 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0[s] | loss 0.71 | epoch 432 | iter 1 / 2 | time 0[s] | loss 0.74 | epoch 433 | iter 1 / 2 | time 0[s] | loss 0.63 | epoch 434 | iter 1 / 2 | time 0[s] | loss 0.85 | epoch 435 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 436 | iter 1 / 2 | time 0[s] | loss 0.74 | epoch 437 | iter 1 / 2 | time 0[s] | loss 0.71 | epoch 438 | iter 1 / 2 | time 0[s] | loss 0.70 | epoch 439 | iter 1 / 2 | time 0[s] | loss 0.70 | epoch 440 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 441 | iter 1 / 2 | time 0[s] | loss 0.80 | epoch 442 | iter 1 / 2 | time 0[s] | loss 0.69 | epoch 443 | iter 1 / 2 | time 0[s] | loss 0.69 | epoch 444 | iter 1 / 2 | time 0[s] | loss 0.74 | epoch 445 | iter 1 / 2 | time 0[s] | loss 0.65 | epoch 446 | iter 1 / 2 | time 0[s] | loss 0.68 | epoch 447 | iter 1 / 2 | time 0[s] | loss 0.70 | epoch 448 | iter 1 / 2 | time 0[s] | loss 0.72 | epoch 449 | iter 1 / 2 | time 0[s] | loss 0.65 | epoch 450 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 451 | iter 1 / 2 | time 0[s] | loss 0.68 | epoch 452 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 453 | iter 1 / 2 | time 0[s] | loss 0.63 | epoch 454 | iter 1 / 2 | time 0[s] | loss 0.85 | epoch 455 | iter 1 / 2 | time 0[s] | loss 0.61 | epoch 456 | iter 1 / 2 | time 0[s] | loss 0.58 | epoch 457 | iter 1 / 2 | time 0[s] | loss 0.84 | epoch 458 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 459 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 460 | iter 1 / 2 | time 0[s] | loss 0.78 | epoch 461 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 462 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 463 | iter 1 / 2 | time 0[s] | loss 0.71 | epoch 464 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 465 | iter 1 / 2 | time 0[s] | loss 0.73 | epoch 466 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 467 | iter 1 / 2 | time 0[s] | loss 0.83 | epoch 468 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 469 | iter 1 / 2 | time 0[s] | loss 0.66 | epoch 470 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 471 | iter 1 / 2 | time 0[s] | loss 0.71 | epoch 472 | iter 1 / 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0.52 | epoch 493 | iter 1 / 2 | time 0[s] | loss 0.69 | epoch 494 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 495 | iter 1 / 2 | time 0[s] | loss 0.69 | epoch 496 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 497 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 498 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 499 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 500 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 501 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 502 | iter 1 / 2 | time 0[s] | loss 0.61 | epoch 503 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 504 | iter 1 / 2 | time 0[s] | loss 0.60 | epoch 505 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 506 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 507 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 508 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 509 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 510 | iter 1 / 2 | time 0[s] | loss 0.51 | epoch 511 | iter 1 / 2 | time 0[s] | loss 0.77 | epoch 512 | iter 1 / 2 | time 0[s] | loss 0.60 | epoch 513 | 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epoch 554 | iter 1 / 2 | time 0[s] | loss 0.63 | epoch 555 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 556 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 557 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 558 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 559 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 560 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 561 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 562 | iter 1 / 2 | time 0[s] | loss 0.63 | epoch 563 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 564 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 565 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 566 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 567 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 568 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 569 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 570 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 571 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 572 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 573 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 574 | iter 1 / 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0[s] | loss 0.55 | epoch 636 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 637 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 638 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 639 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 640 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 641 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 642 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 643 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 644 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 645 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 646 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 647 | iter 1 / 2 | time 0[s] | loss 0.66 | epoch 648 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 649 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 650 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 651 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 652 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 653 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 654 | iter 1 / 2 | time 0[s] | loss 0.28 | epoch 655 | iter 1 / 2 | time 0[s] | loss 0.58 | epoch 656 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 657 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 658 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 659 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 660 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 661 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 662 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 663 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 664 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 665 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 666 | iter 1 / 2 | time 0[s] | loss 0.38 | epoch 667 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 668 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 669 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 670 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 671 | iter 1 / 2 | time 0[s] | loss 0.58 | epoch 672 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 673 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 674 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 675 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 676 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 677 | iter 1 / 2 | time 0[s] | loss 0.66 | epoch 678 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 679 | iter 1 / 2 | time 0[s] | loss 0.47 | epoch 680 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 681 | iter 1 / 2 | time 0[s] | loss 0.58 | epoch 682 | iter 1 / 2 | time 0[s] | loss 0.30 | epoch 683 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 684 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 685 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 686 | iter 1 / 2 | time 0[s] | loss 0.67 | epoch 687 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 688 | iter 1 / 2 | time 0[s] | loss 0.52 | epoch 689 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 690 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 691 | iter 1 / 2 | time 0[s] | loss 0.52 | epoch 692 | iter 1 / 2 | time 0[s] | loss 0.27 | epoch 693 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 694 | iter 1 / 2 | time 0[s] | loss 0.52 | epoch 695 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 696 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 697 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 698 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 699 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 700 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 701 | iter 1 / 2 | time 0[s] | loss 0.47 | epoch 702 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 703 | iter 1 / 2 | time 0[s] | loss 0.56 | epoch 704 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 705 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 706 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 707 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 708 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 709 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 710 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 711 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 712 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 713 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 714 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 715 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 716 | iter 1 / 2 | time 0[s] | loss 0.51 | epoch 717 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 718 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 719 | iter 1 / 2 | time 0[s] | loss 0.20 | epoch 720 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 721 | iter 1 / 2 | time 0[s] | loss 0.52 | epoch 722 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 723 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 724 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 725 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 726 | iter 1 / 2 | time 0[s] | loss 0.63 | epoch 727 | iter 1 / 2 | time 0[s] | loss 0.18 | epoch 728 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 729 | iter 1 / 2 | time 0[s] | loss 0.52 | epoch 730 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 731 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 732 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 733 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 734 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 735 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 736 | iter 1 / 2 | time 0[s] | loss 0.20 | epoch 737 | iter 1 / 2 | time 0[s] | loss 0.61 | epoch 738 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 739 | iter 1 / 2 | time 0[s] | loss 0.30 | epoch 740 | iter 1 / 2 | time 0[s] | loss 0.30 | epoch 741 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 742 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 743 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 744 | iter 1 / 2 | time 0[s] | loss 0.28 | epoch 745 | iter 1 / 2 | time 0[s] | loss 0.52 | epoch 746 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 747 | iter 1 / 2 | time 0[s] | loss 0.28 | epoch 748 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 749 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 750 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 751 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 752 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 753 | iter 1 / 2 | time 0[s] | loss 0.29 | epoch 754 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 755 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 756 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 757 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 758 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 759 | iter 1 / 2 | time 0[s] | loss 0.19 | epoch 760 | iter 1 / 2 | time 0[s] | loss 0.53 | epoch 761 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 762 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 763 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 764 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 765 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 766 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 767 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 768 | iter 1 / 2 | time 0[s] | loss 0.38 | epoch 769 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 770 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 771 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 772 | iter 1 / 2 | time 0[s] | loss 0.51 | epoch 773 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 774 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 775 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 776 | iter 1 / 2 | time 0[s] | loss 0.41 | epoch 777 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 778 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 779 | iter 1 / 2 | time 0[s] | loss 0.28 | epoch 780 | iter 1 / 2 | time 0[s] | loss 0.62 | epoch 781 | iter 1 / 2 | time 0[s] | loss 0.28 | epoch 782 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 783 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 784 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 785 | iter 1 / 2 | time 0[s] | loss 0.29 | epoch 786 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 787 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 788 | iter 1 / 2 | time 0[s] | loss 0.19 | epoch 789 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 790 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 791 | iter 1 / 2 | time 0[s] | loss 0.29 | epoch 792 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 793 | iter 1 / 2 | time 0[s] | loss 0.60 | epoch 794 | iter 1 / 2 | time 0[s] | loss 0.27 | epoch 795 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 796 | iter 1 / 2 | time 0[s] | loss 0.27 | epoch 797 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 798 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 799 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 800 | iter 1 / 2 | time 0[s] | loss 0.29 | epoch 801 | iter 1 / 2 | time 0[s] | loss 0.38 | epoch 802 | iter 1 / 2 | time 0[s] | loss 0.59 | epoch 803 | iter 1 / 2 | time 0[s] | loss 0.29 | epoch 804 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 805 | iter 1 / 2 | time 0[s] | loss 0.30 | epoch 806 | iter 1 / 2 | time 0[s] | loss 0.24 | epoch 807 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 808 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 809 | iter 1 / 2 | time 0[s] | loss 0.51 | epoch 810 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 811 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 812 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 813 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 814 | iter 1 / 2 | time 0[s] | loss 0.38 | epoch 815 | iter 1 / 2 | time 0[s] | loss 0.27 | epoch 816 | iter 1 / 2 | time 0[s] | loss 0.40 | epoch 817 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 818 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 819 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 820 | iter 1 / 2 | time 0[s] | loss 0.50 | epoch 821 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 822 | iter 1 / 2 | time 0[s] | loss 0.47 | epoch 823 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 824 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 825 | iter 1 / 2 | time 0[s] | loss 0.17 | epoch 826 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 827 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 828 | iter 1 / 2 | time 0[s] | loss 0.29 | epoch 829 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 830 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 831 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 832 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 833 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 834 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 835 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 836 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 837 | iter 1 / 2 | time 0[s] | loss 0.39 | epoch 838 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 839 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 840 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 841 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 842 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 843 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 844 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 845 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 846 | iter 1 / 2 | time 0[s] | loss 0.47 | epoch 847 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 848 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 849 | iter 1 / 2 | time 0[s] | loss 0.58 | epoch 850 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 851 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 852 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 853 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 854 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 855 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 856 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 857 | iter 1 / 2 | time 0[s] | loss 0.57 | epoch 858 | iter 1 / 2 | time 0[s] | loss 0.27 | epoch 859 | iter 1 / 2 | time 0[s] | loss 0.47 | epoch 860 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 861 | iter 1 / 2 | time 0[s] | loss 0.49 | epoch 862 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 863 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 864 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 865 | iter 1 / 2 | time 0[s] | loss 0.27 | epoch 866 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 867 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 868 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 869 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 870 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 871 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 872 | iter 1 / 2 | time 0[s] | loss 0.47 | epoch 873 | iter 1 / 2 | time 0[s] | loss 0.37 | epoch 874 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 875 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 876 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 877 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 878 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 879 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 880 | iter 1 / 2 | time 0[s] | loss 0.24 | epoch 881 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 882 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 883 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 884 | iter 1 / 2 | time 0[s] | loss 0.13 | epoch 885 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 886 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 887 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 888 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 889 | iter 1 / 2 | time 0[s] | loss 0.26 | epoch 890 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 891 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 892 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 893 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 894 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 895 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 896 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 897 | iter 1 / 2 | time 0[s] | loss 0.48 | epoch 898 | iter 1 / 2 | time 0[s] | loss 0.24 | epoch 899 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 900 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 901 | iter 1 / 2 | time 0[s] | loss 0.24 | epoch 902 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 903 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 904 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 905 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 906 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 907 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 908 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 909 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 910 | iter 1 / 2 | time 0[s] | loss 0.24 | epoch 911 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 912 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 913 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 914 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 915 | iter 1 / 2 | time 0[s] | loss 0.36 | epoch 916 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 917 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 918 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 919 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 920 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 921 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 922 | iter 1 / 2 | time 0[s] | loss 0.25 | epoch 923 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 924 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 925 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 926 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 927 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 928 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 929 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 930 | iter 1 / 2 | time 0[s] | loss 0.21 | epoch 931 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 932 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 933 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 934 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 935 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 936 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 937 | iter 1 / 2 | time 0[s] | loss 0.35 | epoch 938 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 939 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 940 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 941 | iter 1 / 2 | time 0[s] | loss 0.46 | epoch 942 | iter 1 / 2 | time 0[s] | loss 0.21 | epoch 943 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 944 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 945 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 946 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 947 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 948 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 949 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 950 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 951 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 952 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 953 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 954 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 955 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 956 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 957 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 958 | iter 1 / 2 | time 0[s] | loss 0.20 | epoch 959 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 960 | iter 1 / 2 | time 0[s] | loss 0.21 | epoch 961 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 962 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 963 | iter 1 / 2 | time 0[s] | loss 0.21 | epoch 964 | iter 1 / 2 | time 0[s] | loss 0.45 | epoch 965 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 966 | iter 1 / 2 | time 0[s] | loss 0.20 | epoch 967 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 968 | iter 1 / 2 | time 0[s] | loss 0.34 | epoch 969 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 970 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 971 | iter 1 / 2 | time 0[s] | loss 0.23 | epoch 972 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 973 | iter 1 / 2 | time 0[s] | loss 0.21 | epoch 974 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 975 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 976 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 977 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 978 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 979 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 980 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 981 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 982 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 983 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 984 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 985 | iter 1 / 2 | time 0[s] | loss 0.11 | epoch 986 | iter 1 / 2 | time 0[s] | loss 0.44 | epoch 987 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 988 | iter 1 / 2 | time 0[s] | loss 0.21 | epoch 989 | iter 1 / 2 | time 0[s] | loss 0.42 | epoch 990 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 991 | iter 1 / 2 | time 0[s] | loss 0.43 | epoch 992 | iter 1 / 2 | time 0[s] | loss 0.32 | epoch 993 | iter 1 / 2 | time 0[s] | loss 0.20 | epoch 994 | iter 1 / 2 | time 0[s] | loss 0.33 | epoch 995 | iter 1 / 2 | time 0[s] | loss 0.54 | epoch 996 | iter 1 / 2 | time 0[s] | loss 0.09 | epoch 997 | iter 1 / 2 | time 0[s] | loss 0.55 | epoch 998 | iter 1 / 2 | time 0[s] | loss 0.31 | epoch 999 | iter 1 / 2 | time 0[s] | loss 0.22 | epoch 1000 | iter 1 / 2 | time 0[s] | loss 0.32
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
you [ 1.0177808 1.038972 -1.7848402 -0.9421127 -1.0638177] say [-1.1361907 -1.1038435 -1.276322 0.41686428 1.12826 ] goodbye [ 0.98462534 0.9692038 0.8629397 -1.0154635 -0.9850802 ] and [-0.73020494 -0.76266265 -1.1803812 1.9313055 0.686535 ] i [ 0.9527775 0.97534245 0.8744229 -1.007618 -0.98118937] hello [ 1.0205678 1.0385231 -1.8062541 -0.97187203 -1.0405105 ] . [-1.2150259 -1.168062 -0.9527735 -1.9033735 1.1629405]
skip-gramもでるはCBOWモデルと異なり、