我正在为NLP任务进行深度学习的最终论文研究，例如中国的老挝语分词和pos标签。
但是我训练了300个模型，然后val_accuracy和损失都没有改变。
我尝试了许多超级参数优化，但没有进行任何更改。以下是我的培训：
模型设置：
Batch_size=32
num_step=60
valid_split=0.1
另一件事。我在深度学习方面也很新，所以我无力解决。
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Layer (type) Output Shape Param #
embedding_1 (Embedding) (None, 60, 7) 441
bidirectional_1 (Bidirection (None, 60, 192) 79872
dropout_1 (Dropout) (None, 60, 192) 0
bidirectional_2 (Bidirection (None, 60, 256) 328704
dropout_2 (Dropout) (None, 60, 256) 0
bidirectional_3 (Bidirection (None, 60, 128) 164352
dropout_3 (Dropout) (None, 60, 128) 0
bidirectional_4 (Bidirection (None, 60, 192) 172800
dropout_4 (Dropout) (None, 60, 192) 0
time_distributed_1 (TimeDist (None, 60, 40) 7720
Total params: 753,889 Trainable params: 753,889 Non-trainable params: 0
Train on 3813 samples, validate on 424 samples
Epoch 1/200 3813/3813 [==============================] - 236s 62ms/step - loss: 1.2699 - categorical_accuracy: 0.7403 - val_loss: 1.0956 - val_categorical_accuracy: 0.7759 | pos_accuracy: 0.0000 || pos_fmeasure: 0.0000 || seg_accuracy: 0.0000 || seg_fmeasure: 0.0000 |
Epoch 2/200 3813/3813 [==============================] - 68s 18ms/step - loss: 1.1891 - categorical_accuracy: 0.7464 - val_loss: 1.0930 - val_categorical_accuracy: 0.7759 | pos_accuracy: 0.0000 || pos_fmeasure: 0.0000 || seg_accuracy: 0.0000 || seg_fmeasure: 0.0000 |
Epoch 3/200 3813/3813 [==============================] - 66s 17ms/step - loss: 1.1799 - categorical_accuracy: 0.7460 - val_loss: 1.0521 - val_categorical_accuracy: 0.7759 | pos_accuracy: 0.0000 || pos_fmeasure: 0.0000 || seg_accuracy: 0.0000 || seg_fmeasure: 0.0000 |
Epoch 4/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.9943 - categorical_accuracy: 0.7625 - val_loss: 1.0329 - val_categorical_accuracy: 0.7755 | pos_accuracy: 0.0003 || pos_fmeasure: 0.0004 || seg_accuracy: 0.0010 || seg_fmeasure: 0.0018 |
Epoch 5/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.7161 - categorical_accuracy: 0.8077 - val_loss: 1.0082 - val_categorical_accuracy: 0.7746 | pos_accuracy: 0.0186 || pos_fmeasure: 0.0213 || seg_accuracy: 0.0371 || seg_fmeasure: 0.0672 |
Epoch 6/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.6510 - categorical_accuracy: 0.8215 - val_loss: 1.0250 - val_categorical_accuracy: 0.7773 | pos_accuracy: 0.0302 || pos_fmeasure: 0.0326 || seg_accuracy: 0.0584 || seg_fmeasure: 0.1046 |
Epoch 7/200 3813/3813 [==============================] - 64s 17ms/step - loss: 0.6089 - categorical_accuracy: 0.8325 - val_loss: 1.0228 - val_categorical_accuracy: 0.7778 | pos_accuracy: 0.0430 || pos_fmeasure: 0.0416 || seg_accuracy: 0.0837 || seg_fmeasure: 0.1469 |
Epoch 8/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.5837 - categorical_accuracy: 0.8391 - val_loss: 1.0205 - val_categorical_accuracy: 0.7765 | pos_accuracy: 0.0454 || pos_fmeasure: 0.0427 || seg_accuracy: 0.0839 || seg_fmeasure: 0.1461 |
Epoch 9/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.5575 - categorical_accuracy: 0.8453 - val_loss: 1.0282 - val_categorical_accuracy: 0.7759 | pos_accuracy: 0.0307 || pos_fmeasure: 0.0327 || seg_accuracy: 0.0548 || seg_fmeasure: 0.0980 |
Epoch 10/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.5326 - categorical_accuracy: 0.8505 - val_loss: 1.0117 - val_categorical_accuracy: 0.7760 | pos_accuracy: 0.0175 || pos_fmeasure: 0.0207 || seg_accuracy: 0.0349 || seg_fmeasure: 0.0626 |
Epoch 11/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.5129 - categorical_accuracy: 0.8554 - val_loss: 1.0031 - val_categorical_accuracy: 0.7758 | pos_accuracy: 0.0226 || pos_fmeasure: 0.0247 || seg_accuracy: 0.0508 || seg_fmeasure: 0.0898 |
Epoch 12/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.4967 - categorical_accuracy: 0.8597 - val_loss: 1.0027 - val_categorical_accuracy: 0.7763 | pos_accuracy: 0.0219 || pos_fmeasure: 0.0227 || seg_accuracy: 0.0563 || seg_fmeasure: 0.0986 |
Epoch 13/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.4817 - categorical_accuracy: 0.8638 - val_loss: 0.9987 - val_categorical_accuracy: 0.7750 | pos_accuracy: 0.0209 || pos_fmeasure: 0.0205 || seg_accuracy: 0.0660 || seg_fmeasure: 0.1145 |
Epoch 14/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.4669 - categorical_accuracy: 0.8674 - val_loss: 0.9921 - val_categorical_accuracy: 0.7754 | pos_accuracy: 0.0243 || pos_fmeasure: 0.0233 || seg_accuracy: 0.0756 || seg_fmeasure: 0.1303 |
Epoch 15/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.4522 - categorical_accuracy: 0.8713 - val_loss: 0.9746 - val_categorical_accuracy: 0.7760 | pos_accuracy: 0.0229 || pos_fmeasure: 0.0245 || seg_accuracy: 0.0650 || seg_fmeasure: 0.1125 |
Epoch 16/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.4415 - categorical_accuracy: 0.8743 - val_loss: 0.9606 - val_categorical_accuracy: 0.7765 | pos_accuracy: 0.0303 || pos_fmeasure: 0.0294 || seg_accuracy: 0.0838 || seg_fmeasure: 0.1434 |
Epoch 17/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.4290 - categorical_accuracy: 0.8776 - val_loss: 0.9696 - val_categorical_accuracy: 0.7754 | pos_accuracy: 0.0218 || pos_fmeasure: 0.0210 || seg_accuracy: 0.0756 || seg_fmeasure: 0.1304 |
Epoch 18/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.4169 - categorical_accuracy: 0.8815 - val_loss: 0.9622 - val_categorical_accuracy: 0.7756 | pos_accuracy: 0.0235 || pos_fmeasure: 0.0221 || seg_accuracy: 0.0789 || seg_fmeasure: 0.1359 |
Epoch 19/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.4070 - categorical_accuracy: 0.8844 - val_loss: 0.9594 - val_categorical_accuracy: 0.7753 | pos_accuracy: 0.0236 || pos_fmeasure: 0.0220 || seg_accuracy: 0.0846 || seg_fmeasure: 0.1438 |
Epoch 20/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3999 - categorical_accuracy: 0.8863 - val_loss: 0.9760 - val_categorical_accuracy: 0.7724 | pos_accuracy: 0.0145 || pos_fmeasure: 0.0150 || seg_accuracy: 0.0653 || seg_fmeasure: 0.1120 |
Epoch 21/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3885 - categorical_accuracy: 0.8899 - val_loss: 0.9823 - val_categorical_accuracy: 0.7730 | pos_accuracy: 0.0221 || pos_fmeasure: 0.0214 || seg_accuracy: 0.0840 || seg_fmeasure: 0.1433 |
Epoch 22/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3822 - categorical_accuracy: 0.8918 - val_loss: 0.9621 - val_categorical_accuracy: 0.7711 | pos_accuracy: 0.0153 || pos_fmeasure: 0.0151 || seg_accuracy: 0.0774 || seg_fmeasure: 0.1325 |
Epoch 23/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3736 - categorical_accuracy: 0.8935 - val_loss: 0.9642 - val_categorical_accuracy: 0.7694 | pos_accuracy: 0.0201 || pos_fmeasure: 0.0199 || seg_accuracy: 0.0912 || seg_fmeasure: 0.1554 |
Epoch 24/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3669 - categorical_accuracy: 0.8959 - val_loss: 0.9726 - val_categorical_accuracy: 0.7721 | pos_accuracy: 0.0258 || pos_fmeasure: 0.0275 || seg_accuracy: 0.0915 || seg_fmeasure: 0.1553 |
Epoch 25/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3603 - categorical_accuracy: 0.8968 - val_loss: 0.9550 - val_categorical_accuracy: 0.7712 | pos_accuracy: 0.0274 || pos_fmeasure: 0.0264 || seg_accuracy: 0.1088 || seg_fmeasure: 0.1841 |
Epoch 26/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3545 - categorical_accuracy: 0.8990 - val_loss: 0.9636 - val_categorical_accuracy: 0.7722 | pos_accuracy: 0.0364 || pos_fmeasure: 0.0358 || seg_accuracy: 0.1162 || seg_fmeasure: 0.1931 |
Epoch 27/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.3469 - categorical_accuracy: 0.9009 - val_loss: 0.9421 - val_categorical_accuracy: 0.7741 | pos_accuracy: 0.0408 || pos_fmeasure: 0.0400 || seg_accuracy: 0.1250 || seg_fmeasure: 0.2081 |
Epoch 28/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.3417 - categorical_accuracy: 0.9026 - val_loss: 0.9452 - val_categorical_accuracy: 0.7719 | pos_accuracy: 0.0397 || pos_fmeasure: 0.0369 || seg_accuracy: 0.1296 || seg_fmeasure: 0.2143 |
Epoch 29/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.3356 - categorical_accuracy: 0.9038 - val_loss: 0.9799 - val_categorical_accuracy: 0.7708 | pos_accuracy: 0.0261 || pos_fmeasure: 0.0275 || seg_accuracy: 0.1029 || seg_fmeasure: 0.1745 |
Epoch 30/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3310 - categorical_accuracy: 0.9060 - val_loss: 0.9598 - val_categorical_accuracy: 0.7678 | pos_accuracy: 0.0367 || pos_fmeasure: 0.0346 || seg_accuracy: 0.1413 || seg_fmeasure: 0.2332 |
Epoch 31/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.3259 - categorical_accuracy: 0.9071 - val_loss: 0.9549 - val_categorical_accuracy: 0.7654 | pos_accuracy: 0.0489 || pos_fmeasure: 0.0428 || seg_accuracy: 0.1668 || seg_fmeasure: 0.2710 |
Epoch 32/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3192 - categorical_accuracy: 0.9080 - val_loss: 0.9451 - val_categorical_accuracy: 0.7682 | pos_accuracy: 0.0351 || pos_fmeasure: 0.0342 || seg_accuracy: 0.1321 || seg_fmeasure: 0.2195 |
Epoch 33/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3169 - categorical_accuracy: 0.9096 - val_loss: 0.9571 - val_categorical_accuracy: 0.7649 | pos_accuracy: 0.0430 || pos_fmeasure: 0.0434 || seg_accuracy: 0.1562 || seg_fmeasure: 0.2561 |
Epoch 34/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.3106 - categorical_accuracy: 0.9105 - val_loss: 0.9457 - val_categorical_accuracy: 0.7708 | pos_accuracy: 0.0441 || pos_fmeasure: 0.0432 || seg_accuracy: 0.1355 || seg_fmeasure: 0.2238 |
Epoch 35/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.3102 - categorical_accuracy: 0.9116 - val_loss: 0.9490 - val_categorical_accuracy: 0.7732 | pos_accuracy: 0.0434 || pos_fmeasure: 0.0425 || seg_accuracy: 0.1322 || seg_fmeasure: 0.2193 |
Epoch 36/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.3013 - categorical_accuracy: 0.9130 - val_loss: 0.9574 - val_categorical_accuracy: 0.7673 | pos_accuracy: 0.0504 || pos_fmeasure: 0.0545 || seg_accuracy: 0.1491 || seg_fmeasure: 0.2463 |
Epoch 37/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.2993 - categorical_accuracy: 0.9139 - val_loss: 0.9473 - val_categorical_accuracy: 0.7722 | pos_accuracy: 0.0487 || pos_fmeasure: 0.0492 || seg_accuracy: 0.1357 || seg_fmeasure: 0.2249 |
Epoch 38/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2981 - categorical_accuracy: 0.9146 - val_loss: 0.9407 - val_categorical_accuracy: 0.7689 | pos_accuracy: 0.0618 || pos_fmeasure: 0.0603 || seg_accuracy: 0.1693 || seg_fmeasure: 0.2749 |
Epoch 39/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2934 - categorical_accuracy: 0.9162 - val_loss: 0.9597 - val_categorical_accuracy: 0.7652 | pos_accuracy: 0.0595 || pos_fmeasure: 0.0580 || seg_accuracy: 0.1724 || seg_fmeasure: 0.2793 |
Epoch 40/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2908 - categorical_accuracy: 0.9163 - val_loss: 0.9835 - val_categorical_accuracy: 0.7622 | pos_accuracy: 0.0482 || pos_fmeasure: 0.0524 || seg_accuracy: 0.1484 || seg_fmeasure: 0.2451 |
Epoch 41/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2867 - categorical_accuracy: 0.9183 - val_loss: 0.9906 - val_categorical_accuracy: 0.7653 | pos_accuracy: 0.0452 || pos_fmeasure: 0.0487 || seg_accuracy: 0.1457 || seg_fmeasure: 0.2410 |
Epoch 42/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2850 - categorical_accuracy: 0.9179 - val_loss: 0.9705 - val_categorical_accuracy: 0.7634 | pos_accuracy: 0.0412 || pos_fmeasure: 0.0437 || seg_accuracy: 0.1427 || seg_fmeasure: 0.2363 |
Epoch 43/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.2805 - categorical_accuracy: 0.9201 - val_loss: 0.9692 - val_categorical_accuracy: 0.7619 | pos_accuracy: 0.0475 || pos_fmeasure: 0.0503 || seg_accuracy: 0.1519 || seg_fmeasure: 0.2498 |
Epoch 44/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2768 - categorical_accuracy: 0.9208 - val_loss: 0.9601 - val_categorical_accuracy: 0.7634 | pos_accuracy: 0.0454 || pos_fmeasure: 0.0496 || seg_accuracy: 0.1496 || seg_fmeasure: 0.2465 |
Epoch 45/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.2743 - categorical_accuracy: 0.9210 - val_loss: 0.9910 - val_categorical_accuracy: 0.7646 | pos_accuracy: 0.0350 || pos_fmeasure: 0.0372 || seg_accuracy: 0.1244 || seg_fmeasure: 0.2092 |
Epoch 46/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2723 - categorical_accuracy: 0.9218 - val_loss: 0.9529 - val_categorical_accuracy: 0.7667 | pos_accuracy: 0.0413 || pos_fmeasure: 0.0421 || seg_accuracy: 0.1386 || seg_fmeasure: 0.2285 |
Epoch 47/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2707 - categorical_accuracy: 0.9220 - val_loss: 0.9646 - val_categorical_accuracy: 0.7612 | pos_accuracy: 0.0392 || pos_fmeasure: 0.0429 || seg_accuracy: 0.1446 || seg_fmeasure: 0.2383 |
Epoch 48/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2655 - categorical_accuracy: 0.9232 - val_loss: 0.9603 - val_categorical_accuracy: 0.7603 | pos_accuracy: 0.0463 || pos_fmeasure: 0.0493 || seg_accuracy: 0.1509 || seg_fmeasure: 0.2487 |
Epoch 49/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2648 - categorical_accuracy: 0.9241 - val_loss: 0.9766 - val_categorical_accuracy: 0.7621 | pos_accuracy: 0.0337 || pos_fmeasure: 0.0372 || seg_accuracy: 0.1295 || seg_fmeasure: 0.2170 |
Epoch 50/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2621 - categorical_accuracy: 0.9246 - val_loss: 0.9651 - val_categorical_accuracy: 0.7619 | pos_accuracy: 0.0355 || pos_fmeasure: 0.0401 || seg_accuracy: 0.1326 || seg_fmeasure: 0.2218 |
Epoch 51/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2595 - categorical_accuracy: 0.9249 - val_loss: 0.9753 - val_categorical_accuracy: 0.7610 | pos_accuracy: 0.0344 || pos_fmeasure: 0.0401 || seg_accuracy: 0.1260 || seg_fmeasure: 0.2113 |
Epoch 52/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2566 - categorical_accuracy: 0.9260 - val_loss: 0.9858 - val_categorical_accuracy: 0.7566 | pos_accuracy: 0.0335 || pos_fmeasure: 0.0359 || seg_accuracy: 0.1342 || seg_fmeasure: 0.2241 |
Epoch 53/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.2551 - categorical_accuracy: 0.9259 - val_loss: 0.9603 - val_categorical_accuracy: 0.7631 | pos_accuracy: 0.0441 || pos_fmeasure: 0.0485 || seg_accuracy: 0.1456 || seg_fmeasure: 0.2412 |
Epoch 54/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.2508 - categorical_accuracy: 0.9274 - val_loss: 0.9743 - val_categorical_accuracy: 0.7578 | pos_accuracy: 0.0454 || pos_fmeasure: 0.0498 || seg_accuracy: 0.1541 || seg_fmeasure: 0.2540 |
Epoch 55/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.2504 - categorical_accuracy: 0.9276 - val_loss: 0.9661 - val_categorical_accuracy: 0.7635 | pos_accuracy: 0.0411 || pos_fmeasure: 0.0468 || seg_accuracy: 0.1311 || seg_fmeasure: 0.2196 |
Epoch 56/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2509 - categorical_accuracy: 0.9275 - val_loss: 0.9656 - val_categorical_accuracy: 0.7665 | pos_accuracy: 0.0424 || pos_fmeasure: 0.0476 || seg_accuracy: 0.1341 || seg_fmeasure: 0.2236 |
Epoch 57/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2493 - categorical_accuracy: 0.9280 - val_loss: 0.9627 - val_categorical_accuracy: 0.7597 | pos_accuracy: 0.0525 || pos_fmeasure: 0.0576 || seg_accuracy: 0.1599 || seg_fmeasure: 0.2615 |
Epoch 58/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2465 - categorical_accuracy: 0.9291 - val_loss: 0.9650 - val_categorical_accuracy: 0.7587 | pos_accuracy: 0.0558 || pos_fmeasure: 0.0600 || seg_accuracy: 0.1656 || seg_fmeasure: 0.2701 |
Epoch 59/200 3813/3813 [==============================] - 64s 17ms/step - loss: 0.2425 - categorical_accuracy: 0.9302 - val_loss: 0.9576 - val_categorical_accuracy: 0.7567 | pos_accuracy: 0.0549 || pos_fmeasure: 0.0600 || seg_accuracy: 0.1719 || seg_fmeasure: 0.2806 |
Epoch 60/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2435 - categorical_accuracy: 0.9302 - val_loss: 0.9722 - val_categorical_accuracy: 0.7606 | pos_accuracy: 0.0460 || pos_fmeasure: 0.0538 || seg_accuracy: 0.1400 || seg_fmeasure: 0.2338 |
Epoch 61/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2426 - categorical_accuracy: 0.9299 - val_loss: 0.9965 - val_categorical_accuracy: 0.7579 | pos_accuracy: 0.0440 || pos_fmeasure: 0.0515 || seg_accuracy: 0.1377 || seg_fmeasure: 0.2308 |
Epoch 62/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.2423 - categorical_accuracy: 0.9304 - val_loss: 0.9749 - val_categorical_accuracy: 0.7583 | pos_accuracy: 0.0474 || pos_fmeasure: 0.0535 || seg_accuracy: 0.1509 || seg_fmeasure: 0.2495 |
Epoch 63/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2395 - categorical_accuracy: 0.9310 - val_loss: 0.9695 - val_categorical_accuracy: 0.7566 | pos_accuracy: 0.0577 || pos_fmeasure: 0.0622 || seg_accuracy: 0.1808 || seg_fmeasure: 0.2937 |
Epoch 64/200 3813/3813 [==============================] - 65s 17ms/step - loss: 0.2416 - categorical_accuracy: 0.9305 - val_loss: 0.9653 - val_categorical_accuracy: 0.7593 | pos_accuracy: 0.0560 || pos_fmeasure: 0.0621 || seg_accuracy: 0.1582 || seg_fmeasure: 0.2601 |
Epoch 65/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2375 - categorical_accuracy: 0.9312 - val_loss: 0.9836 - val_categorical_accuracy: 0.7539 | pos_accuracy: 0.0579 || pos_fmeasure: 0.0628 || seg_accuracy: 0.1661 || seg_fmeasure: 0.2726 |
Epoch 66/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2370 - categorical_accuracy: 0.9316 - val_loss: 0.9850 - val_categorical_accuracy: 0.7536 | pos_accuracy: 0.0552 || pos_fmeasure: 0.0621 || seg_accuracy: 0.1600 || seg_fmeasure: 0.2645 |
Epoch 67/200 3813/3813 [==============================] - 66s 17ms/step - loss: 0.2335 - categorical_accuracy: 0.9325 - val_loss: 1.0054 - val_categorical_accuracy: 0.7432 | pos_accuracy: 0.0516 || pos_fmeasure: 0.0556 || seg_accuracy: 0.1761 || seg_fmeasure: 0.2889 |
Epoch 68/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2333 - categorical_accuracy: 0.9331 - val_loss: 0.9817 - val_categorical_accuracy: 0.7521 | pos_accuracy: 0.0507 || pos_fmeasure: 0.0566 || seg_accuracy: 0.1500 || seg_fmeasure: 0.2479 |
Epoch 69/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2320 - categorical_accuracy: 0.9334 - val_loss: 0.9924 - val_categorical_accuracy: 0.7513 | pos_accuracy: 0.0463 || pos_fmeasure: 0.0514 || seg_accuracy: 0.1512 || seg_fmeasure: 0.2509 |
Epoch 70/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2339 - categorical_accuracy: 0.9328 - val_loss: 0.9700 - val_categorical_accuracy: 0.7537 | pos_accuracy: 0.0532 || pos_fmeasure: 0.0595 || seg_accuracy: 0.1613 || seg_fmeasure: 0.2650 |
Epoch 71/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.2357 - categorical_accuracy: 0.9330 - val_loss: 1.0053 - val_categorical_accuracy: 0.7464 | pos_accuracy: 0.0500 || pos_fmeasure: 0.0567 || seg_accuracy: 0.1663 || seg_fmeasure: 0.2734 |
Epoch 72/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.2307 - categorical_accuracy: 0.9338 - val_loss: 0.9987 - val_categorical_accuracy: 0.7469 | pos_accuracy: 0.0468 || pos_fmeasure: 0.0501 || seg_accuracy: 0.1608 || seg_fmeasure: 0.2644 |
Epoch 73/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2280 - categorical_accuracy: 0.9341 - val_loss: 0.9946 - val_categorical_accuracy: 0.7465 | pos_accuracy: 0.0495 || pos_fmeasure: 0.0550 || seg_accuracy: 0.1665 || seg_fmeasure: 0.2733 |
Epoch 74/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2303 - categorical_accuracy: 0.9342 - val_loss: 1.0042 - val_categorical_accuracy: 0.7536 | pos_accuracy: 0.0471 || pos_fmeasure: 0.0533 || seg_accuracy: 0.1488 || seg_fmeasure: 0.2464 |
Epoch 75/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2273 - categorical_accuracy: 0.9352 - val_loss: 1.0256 - val_categorical_accuracy: 0.7439 | pos_accuracy: 0.0519 || pos_fmeasure: 0.0559 || seg_accuracy: 0.1708 || seg_fmeasure: 0.2786 |
Epoch 76/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2279 - categorical_accuracy: 0.9350 - val_loss: 1.0066 - val_categorical_accuracy: 0.7497 | pos_accuracy: 0.0572 || pos_fmeasure: 0.0619 || seg_accuracy: 0.1738 || seg_fmeasure: 0.2823 |
Epoch 77/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2254 - categorical_accuracy: 0.9351 - val_loss: 1.0302 - val_categorical_accuracy: 0.7460 | pos_accuracy: 0.0519 || pos_fmeasure: 0.0552 || seg_accuracy: 0.1727 || seg_fmeasure: 0.2817 |
Epoch 78/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2241 - categorical_accuracy: 0.9362 - val_loss: 0.9972 - val_categorical_accuracy: 0.7479 | pos_accuracy: 0.0582 || pos_fmeasure: 0.0641 || seg_accuracy: 0.1757 || seg_fmeasure: 0.2859 |
Epoch 79/200 3813/3813 [==============================] - 67s 17ms/step - loss: 0.2205 - categorical_accuracy: 0.9365 - val_loss: 0.9952 - val_categorical_accuracy: 0.7495 | pos_accuracy: 0.0517 || pos_fmeasure: 0.0558 || seg_accuracy: 0.1672 || seg_fmeasure: 0.2732 |
Epoch 80/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2192 - categorical_accuracy: 0.9373 - val_loss: 0.9702 - val_categorical_accuracy: 0.7568 | pos_accuracy: 0.0602 || pos_fmeasure: 0.0659 || seg_accuracy: 0.1690 || seg_fmeasure: 0.2754 |
Epoch 81/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2167 - categorical_accuracy: 0.9375 - val_loss: 1.0032 - val_categorical_accuracy: 0.7475 | pos_accuracy: 0.0615 || pos_fmeasure: 0.0651 || seg_accuracy: 0.1897 || seg_fmeasure: 0.3063 |
Epoch 82/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2195 - categorical_accuracy: 0.9370 - val_loss: 1.0044 - val_categorical_accuracy: 0.7483 | pos_accuracy: 0.0630 || pos_fmeasure: 0.0659 || seg_accuracy: 0.1836 || seg_fmeasure: 0.2975 |
Epoch 83/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2173 - categorical_accuracy: 0.9377 - val_loss: 0.9839 - val_categorical_accuracy: 0.7501 | pos_accuracy: 0.0640 || pos_fmeasure: 0.0696 || seg_accuracy: 0.1835 || seg_fmeasure: 0.2974 |
Epoch 84/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2173 - categorical_accuracy: 0.9378 - val_loss: 0.9974 - val_categorical_accuracy: 0.7460 | pos_accuracy: 0.0642 || pos_fmeasure: 0.0696 || seg_accuracy: 0.1891 || seg_fmeasure: 0.3056 |
Epoch 85/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2175 - categorical_accuracy: 0.9381 - val_loss: 0.9861 - val_categorical_accuracy: 0.7504 | pos_accuracy: 0.0616 || pos_fmeasure: 0.0653 || seg_accuracy: 0.1772 || seg_fmeasure: 0.2883 |
Epoch 86/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2122 - categorical_accuracy: 0.9389 - val_loss: 1.0061 - val_categorical_accuracy: 0.7454 | pos_accuracy: 0.0558 || pos_fmeasure: 0.0608 || seg_accuracy: 0.1711 || seg_fmeasure: 0.2801 |
Epoch 87/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2138 - categorical_accuracy: 0.9388 - val_loss: 0.9852 - val_categorical_accuracy: 0.7496 | pos_accuracy: 0.0686 || pos_fmeasure: 0.0744 || seg_accuracy: 0.1834 || seg_fmeasure: 0.2964 |
Epoch 88/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2114 - categorical_accuracy: 0.9390 - val_loss: 0.9954 - val_categorical_accuracy: 0.7519 | pos_accuracy: 0.0709 || pos_fmeasure: 0.0760 || seg_accuracy: 0.1925 || seg_fmeasure: 0.3090 |
Epoch 89/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2107 - categorical_accuracy: 0.9393 - val_loss: 1.0079 - val_categorical_accuracy: 0.7432 | pos_accuracy: 0.0677 || pos_fmeasure: 0.0729 || seg_accuracy: 0.1956 || seg_fmeasure: 0.3142 |
Epoch 90/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2129 - categorical_accuracy: 0.9389 - val_loss: 1.0169 - val_categorical_accuracy: 0.7514 | pos_accuracy: 0.0612 || pos_fmeasure: 0.0669 || seg_accuracy: 0.1686 || seg_fmeasure: 0.2756 |
Epoch 91/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2100 - categorical_accuracy: 0.9394 - val_loss: 0.9927 - val_categorical_accuracy: 0.7500 | pos_accuracy: 0.0718 || pos_fmeasure: 0.0764 || seg_accuracy: 0.1939 || seg_fmeasure: 0.3103 |
Epoch 92/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2102 - categorical_accuracy: 0.9395 - val_loss: 1.0296 - val_categorical_accuracy: 0.7467 | pos_accuracy: 0.0523 || pos_fmeasure: 0.0562 || seg_accuracy: 0.1637 || seg_fmeasure: 0.2680 |
Epoch 93/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2090 - categorical_accuracy: 0.9397 - val_loss: 1.0183 - val_categorical_accuracy: 0.7450 | pos_accuracy: 0.0608 || pos_fmeasure: 0.0638 || seg_accuracy: 0.1810 || seg_fmeasure: 0.2925 |
Epoch 94/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2061 - categorical_accuracy: 0.9401 - val_loss: 1.0116 - val_categorical_accuracy: 0.7452 | pos_accuracy: 0.0617 || pos_fmeasure: 0.0666 || seg_accuracy: 0.1865 || seg_fmeasure: 0.3021 |
Epoch 95/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2089 - categorical_accuracy: 0.9398 - val_loss: 1.0023 - val_categorical_accuracy: 0.7456 | pos_accuracy: 0.0662 || pos_fmeasure: 0.0699 || seg_accuracy: 0.1803 || seg_fmeasure: 0.2922 |
Epoch 96/200 3813/3813 [==============================] - 68s 18ms/step - loss: 0.2041 - categorical_accuracy: 0.9409 - val_loss: 1.0000 - val_categorical_accuracy: 0.7414 | pos_accuracy: 0.0681 || pos_fmeasure: 0.0706 || seg_accuracy: 0.1943 || seg_fmeasure: 0.3125 |
Epoch 97/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2030 - categorical_accuracy: 0.9414 - val_loss: 1.0076 - val_categorical_accuracy: 0.7426 | pos_accuracy: 0.0712 || pos_fmeasure: 0.0758 || seg_accuracy: 0.1947 || seg_fmeasure: 0.3127 |
Epoch 98/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2079 - categorical_accuracy: 0.9402 - val_loss: 1.0181 - val_categorical_accuracy: 0.7401 | pos_accuracy: 0.0679 || pos_fmeasure: 0.0725 || seg_accuracy: 0.1878 || seg_fmeasure: 0.3032 |
Epoch 99/200 3813/3813 [==============================] - 69s 18ms/step - loss: 0.2056 - categorical_accuracy: 0.9410 - val_loss: 1.0248 - val_categorical_accuracy: 0.7458 | pos_accuracy: 0.0675 || pos_fmeasure: 0.0731 || seg_accuracy: 0.1872 || seg_fmeasure: 0.3019 |
Epoch 100/200 3813/3813 [==============================] - 67s 18ms/step - loss: 0.2065 - categorical_accuracy: 0.9406 - val_loss: 1.0230 - val_categorical_accuracy: 0.7401 | pos_accuracy: 0.0711 || pos_fmeasure: 0.0746 || seg_accuracy: 0.1923 || seg_fmeasure: 0.3093 |