python - keras 模型中损失的 nan 值

Question

我有以下数据形状

X_Train.shape,Y_Train.shape
Out[52]: ((983, 19900), (983,))
X_Test.shape,Y_Test.shape
Out[53]: ((52, 19900), (52,))

我正在运行一个简单的二元分类器，因为 Y_train 和 Y_test 可以是 1 或 2

import  keras
import  tensorflow as tf
from keras import  layers
from keras.layers import Input, Dense
from keras.models import Model,Sequential
import numpy as np
from  keras.optimizers import  Adam

myModel = keras.Sequential([
    keras.layers.Dense(1000,activation=tf.nn.relu,input_shape=(19900,)),
    keras.layers.Dense(64, activation=tf.nn.relu),
    keras.layers.Dense(32, activation=tf.nn.relu),
    keras.layers.Dense(1, activation=tf.nn.softmax)
])

myModel.compile(optimizer='adam', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
myModel.fit(X_Train, Y_Train, epochs=100,batch_size=1000)
test_loss,test_acc=myModel.evaluate(X_Test,Y_Test)

代码输出

训练损失和准确性

Epoch 1/100
983/983 [==============================] - 1s 1ms/step - loss: nan - acc: 0.4608
Epoch 2/100
983/983 [==============================] - 0s 206us/step - loss: nan - acc: 0.4873
Epoch 3/100
983/983 [==============================] - 0s 200us/step - loss: nan - acc: 0.4883
Epoch 4/100
983/983 [==============================] - 0s 197us/step - loss: nan - acc: 0.4883
Epoch 5/100
983/983 [==============================] - 0s 194us/step - loss: nan - acc: 0.4873
Epoch 6/100
983/983 [==============================] - 0s 202us/step - loss: nan - acc: 0.4863
Epoch 7/100
983/983 [==============================] - 0s 198us/step - loss: nan - acc: 0.4863
Epoch 8/100
983/983 [==============================] - 0s 194us/step - loss: nan - acc: 0.4883
Epoch 9/100
983/983 [==============================] - 0s 196us/step - loss: nan - acc: 0.4873
Epoch 10/100
983/983 [==============================] - 0s 198us/step - loss: nan - acc: 0.4873
Epoch 11/100
983/983 [==============================] - 0s 200us/step - loss: nan - acc: 0.4893
Epoch 12/100
983/983 [==============================] - 0s 198us/step - loss: nan - acc: 0.4873
Epoch 13/100
983/983 [==============================] - 0s 194us/step - loss: nan - acc: 0.4873
Epoch 14/100
983/983 [==============================] - 0s 197us/step - loss: nan - acc: 0.4883
Epoch 97/100
    983/983 [==============================] - 0s 196us/step - loss: nan - acc: 0.4893
Epoch 98/100
    983/983 [==============================] - 0s 199us/step - loss: nan - acc: 0.4883
Epoch 99/100
    983/983 [==============================] - 0s 193us/step - loss: nan - acc: 0.4883
Epoch 100/100
    983/983 [==============================] - 0s 196us/step - loss: nan - acc: 0.4863

测试损失和准确性

test_loss,test_acc
Out[58]: (nan, 0.4615384661234342)

我还检查了我的数据中是否有任何 nan 值

np.isnan(X_Train).any()
Out[5]: False
np.isnan(Y_Train).any()
Out[6]: False
np.isnan(X_Test).any()
Out[7]: False
np.isnan(Y_Test).any()
Out[8]: False

我的问题是为什么我的训练准确度没有提高，为什么损失是 nan 还有为什么没有单热编码输出中的 softmax 工作正常？

注1:我很抱歉我的数据很大，所以我不能在这里分享它，但如果有什么方法可以在这里分享它，那么我准备这样做。

注2 我的训练数据中有很多零值

Answer 1

有时与 Keras 的组合 Relu和 Softmax导致数值问题为 Relu可以产生与非常小的概率相对应的大正值。

尝试使用 tanh而不是 Relu