我正在尝试训练神经网络以执行简单的映射，即X到Y。在这种情况下，我试图将值映射到其相应的错误。 X和Y均为大小为1077的数组。
数据有很多噪音，并且这种关系基本上是不存在的。因此，我决定选择分类而不是回归。如果使用MSE（如下所示），则精度永远不会超过零，并且损失是巨大且不变的。
# Data preprocessing
from sklearn.model_selection import train_test_split
A_train, A_test, Aerror_train, Aerror_test = train_test_split(A, A_error,
test_size=0.33, random_state=42)
# Defining and compiling the model
model = Sequential() # initializing a sequential model
model.add(Dense(1, activation = 'relu', input_shape = (1,))) #first layer
model.add(Dropout(0.2))
model.add(Dense(1, activation = 'relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation ='softmax'))
model.summary()
model.compile(loss = 'mean_squared_error', optimizer = 'sgd',
metrics = ['accuracy'] ) #sgd = stochastic gradient descent, cat_ce =
probability that increases as the predicted and actual values diverge
如果我尝试使用损失函数稀疏分类交叉熵，因为我想进行多标签分类
我收到以下错误：
InvalidArgumentError (see above for traceback): Received a label value of
15614 which is outside the valid range of [0, 1).