Question

我正在尝试使用CNN-LSTM进行3类分类，我已经在MATLAB上准备了数据，并使用以下代码进行了训练。它适用于我使用二进制交叉熵的二进制分类，但不适用于我使用sparse_categorical_crossentropy的多类情况。我的损失为nan，准确性为0.0000e + 00。怎么了？

import os 
os.environ["CUDA_DEVİCE_ORDER"] = "PCI_BUS_ID"
 os.environ["CUDA_VISIBLE_DEVICES"] = "1"  

import scipy.io 
import numpy as np 
mat = scipy.io.loadmat('frames3class_train80_test20.mat')
 XTrain = mat['tensortrain'] 
y = mat['YTrain']  
## trainlength=2400 
X = np.zeros(shape=(trainlength,8,18,400,1))
 X[:,:,:,:,0]=XTrain  
 import keras  from keras.layers import Dense, LSTM, Flatten, TimeDistributed, Conv2D, Dropout,MaxPooling2D 
from keras.models import Sequential 
width = 18 
height = 400 
kernelsize =2 
numberOfHiddenUnits =8 
model = Sequential() 
model.add(TimeDistributed(Conv2D(2, (kernelsize, kernelsize), activation='relu'),                           input_shape=(None, width, height, 1)))
 model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2)))) 
model.add(TimeDistributed(Flatten())) 
model.add(LSTM(numberOfHiddenUnits)) 
model.add(Dense(1, activation='sigmoid')) 
opt=keras.optimizers.Adam(learning_rate=0.000001) model.compile(loss=keras.losses.sparse_categorical_crossentropy, optimizer=opt, metrics=['acc']) print(model.summary())  

model.fit(X, y,batch_size=16,epochs=30)

Python控制台上的结果为

  16/2400 [..............................] - ETA: 2s - loss: nan - acc: 0.0000e+00
  80/2400 [>.............................] - ETA: 2s - loss: nan - acc: 0.0000e+00
 144/2400 [>.............................] - ETA: 2s - loss: nan - acc: 0.0000e+00
 208/2400 [=>............................] - ETA: 2s - loss: nan - acc: 0.0000e+00
 272/2400 [==>...........................] - ETA: 2s - loss: nan - acc: 0.0000e+00
 336/2400 [===>..........................] - ETA: 2s - loss: nan - acc: 0.0000e+00
 384/2400 [===>..........................] - ETA: 2s - loss: nan - acc: 0.0000e+00

感谢您的帮助，

Answer 1

好吧，我用一种热编码更改了标签，因此损失函数得以解决。

如何避免损失：CNN-LSTM的nan-acc：0.0000e + 00？

1 个答案: