我正在处理LSTM问题。我试图基于文本分类(有16种人格类型)来[[预测MBTI(Myers-Briggs测试)人格类型。
我有一个csv文件
,该文件已进行了预处理:删除了停用词,对其进行了词形化,标记化,排序和填充。文件没有任何NaN值,并且文本序列只有int数。但是,在尝试训练我得到的模型时会产生问题:loss: nan - accuracy: 0.0000e+00 - val_loss: nan - val_accuracy: 0.0000e+00
根据要求:x,y数据和标签的结果看起来如何
print(validation_label_seq)
[[ 5]
[10]
[ 4]
[ 4]
[15]
[12]
[ 1]...]
print(validation_padded[0])
maxlen = 240
array([ 23, 353, 147, 677, 1, 1, 409, 10, 845, 1530, 1,
103, 107, 998, 117, 1389, 25, 1, 28, 1889, 165, 1,
1520, 49, 718, 65, 55, 34, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...], dtype=int32)
print(train_label_seq)
[[ 8]
[ 9]
[ 3]
[ 7]
[ 4]
[10]
[15]
[11]...]
print(train_data_padded[0])
maxlen = 240
array([ 19, 301, 133, 302, 562, 133, 28, 563, 895, 896, 897, 118, 99,
564, 397, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...], dtype=int32)
results = model.evaluate(validation_padded, validation_label_seq)
test = validation_padded[10]
predict = model.predict_classes([test])
print(predict[1])
59/59 [==============================] - 0s 1ms/sample - loss: nan - accuracy: 0.0000e+00
[0]
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/sequential.py:342: RuntimeWarning: invalid value encountered in greater
return (proba > 0.5).astype('int32')
print(predict)
array([[0],
[0],
...
[0],
[0]], dtype=int32)
我尝试了什么?
预期输出:
1 output:
INTP: 89%
16 outputs:
ENTP: 5% | INTP: 81% | INTJ: 1% | ...
如果您想检查,这里是代码:mbti personality
Dataframe:mbti_df
将考虑任何改善问题的建议
accuracy
指标进行比较可以得出正确的结果。示例:
软最大输出[0.2, 0.8]
其他[0 , 1]
的输出>
然后会出现不匹配,并且准确性会受到影响。