[Question] 文本分类中的CNN开头的模型accuracy不管换数据集还是调参数都只有0.2

[hwq458362228]

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Environment

• OS [e.g. Mac OS, Linux]: Win10
• Python Version: 3.7
• requirements.txt: TensorFlow 2.3 kashgari 2.0.1

[Paste requirements.txt file here]

Question

不管是使用SMP2018ECDTCorpus还是自己的数据集，在使用CNN开头的系列文本分类模型时，这个accuracy都不行，也试过改变学习率和epoch等参数，但是没啥作用，不知道不是这些模型本身有问题

from kashgari.corpus import SMP2018ECDTCorpus
from kashgari.tasks.classification import CNN_Model
from kashgari.callbacks import EvalCallBack

import logging
logging.basicConfig(level='DEBUG')

train_x, train_y = SMP2018ECDTCorpus.load_data('train')
valid_x, valid_y = SMP2018ECDTCorpus.load_data('valid')
test_x, test_y = SMP2018ECDTCorpus.load_data('test')

model = CNN_Model()
model.fit(train_x, train_y, valid_x, valid_y,batch_size=64,epochs=14)
model.evaluate(test_x,test_y,batch_size=64)

运行结果：
2022-04-14 18:08:55,276 [DEBUG] kashgari - loaded 1881 samples from C:\Users\hwq45.kashgari\datasets\SMP2018ECDTCorpus\train.csv. Sample:
x[0]: ['打', '开', '河', '南', '英', '东', '网', '站']
y[0]: website
2022-04-14 18:08:55,280 [DEBUG] kashgari - loaded 418 samples from C:\Users\hwq45.kashgari\datasets\SMP2018ECDTCorpus\valid.csv. Sample:
x[0]: ['来', '一', '首', '，', '灵', '岩', '。']
y[0]: poetry
2022-04-14 18:08:55,284 [DEBUG] kashgari - loaded 770 samples from C:\Users\hwq45.kashgari\datasets\SMP2018ECDTCorpus\test.csv. Sample:
x[0]: ['给', '曹', '广', '义', '打', '电', '话']
y[0]: telephone
Preparing text vocab dict: 100%|██████████| 1881/1881 [00:00<00:00, 943831.30it/s]
Preparing text vocab dict: 100%|██████████| 418/418 [00:00<00:00, 416936.76it/s]
2022-04-14 18:08:55,291 [DEBUG] kashgari - --- Build vocab dict finished, Total: 875 ---
2022-04-14 18:08:55,291 [DEBUG] kashgari - Top-10: ['[PAD]', '[UNK]', '[CLS]', '[SEP]', '的', '么', '我', '。', '怎', '你']
Preparing classification label vocab dict: 100%|██████████| 1881/1881 [00:00<?, ?it/s]
Preparing classification label vocab dict: 100%|██████████| 418/418 [00:00<?, ?it/s]
Calculating sequence length: 100%|██████████| 1881/1881 [00:00<00:00, 1894234.29it/s]
Calculating sequence length: 100%|██████████| 418/418 [00:00<00:00, 419430.40it/s]
2022-04-14 18:08:55,309 [DEBUG] kashgari - Calculated sequence length = 15
2022-04-14 18:08:55,337 [DEBUG] kashgari - Model: "functional_43"

---

Layer (type) Output Shape Param #

input (InputLayer) [(None, None)] 0

---

layer_embedding (Embedding) (None, None, 100) 87500

---

conv1d_6 (Conv1D) (None, None, 128) 64128

---

global_max_pooling1d_4 (Glob (None, 128) 0

---

dense_14 (Dense) (None, 64) 8256

---

dense_15 (Dense) (None, 31) 2015

---

activation_10 (Activation) (None, 31) 0

Total params: 161,899
Trainable params: 161,899
Non-trainable params: 0

---

Epoch 1/14
29/29 [==============================] - 0s 8ms/step - loss: 3.3098 - accuracy: 0.1735 - val_loss: 3.1836 - val_accuracy: 0.1901
Epoch 2/14
29/29 [==============================] - 0s 5ms/step - loss: 3.0778 - accuracy: 0.1992 - val_loss: 3.0883 - val_accuracy: 0.1953
Epoch 3/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0232 - accuracy: 0.1992 - val_loss: 3.0700 - val_accuracy: 0.2005
Epoch 4/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0164 - accuracy: 0.1987 - val_loss: 3.0591 - val_accuracy: 0.1901
Epoch 5/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0395 - accuracy: 0.1943 - val_loss: 3.0622 - val_accuracy: 0.1979
Epoch 6/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0327 - accuracy: 0.2003 - val_loss: 3.0659 - val_accuracy: 0.1875
Epoch 7/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0361 - accuracy: 0.1948 - val_loss: 3.0711 - val_accuracy: 0.1953
Epoch 8/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0347 - accuracy: 0.1987 - val_loss: 3.0581 - val_accuracy: 0.1901
Epoch 9/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0155 - accuracy: 0.1981 - val_loss: 3.0576 - val_accuracy: 0.2005
Epoch 10/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0415 - accuracy: 0.2036 - val_loss: 3.0651 - val_accuracy: 0.1953
Epoch 11/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0296 - accuracy: 0.1992 - val_loss: 3.0850 - val_accuracy: 0.1849
Epoch 12/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0132 - accuracy: 0.2053 - val_loss: 3.0643 - val_accuracy: 0.1953
Epoch 13/14
29/29 [==============================] - 0s 4ms/step - loss: 3.0523 - accuracy: 0.1899 - val_loss: 3.0639 - val_accuracy: 0.2005
Epoch 14/14
29/29 [==============================] - 0s 4ms/step - loss: 3.7734 - accuracy: 0.2075 - val_loss: 3.0653 - val_accuracy: 0.2031

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