如果你这样定义你的数据：

raw = np.array([[41.392953, 41.392889, 41.392825,41.392761, 41.392697],
        [2.163917,2.163995,2.164072,2.164150,2.164229 ]])
X = raw[:,:-1]
y = raw[:,-1]

将有4个特征、2个样本和1个标签的回归：

对于多个输出，只需像这样设置最终层：
model.add(Dense(n_classes, activation='linear'))
n_classes可以是任何你喜欢的数字。

如果你想有两个输出，你必须在你的输出层中显式地指定它们。例如：

from keras.models import Sequential
from keras.layers import Dense

X = tf.random.normal((341, 2))
Y = tf.random.normal((341, 2))
# define and fit the final model
model = Sequential()
model.add(Dense(4, input_dim=2, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(2, activation='linear'))
model.compile(loss='mse', optimizer='adam')
model.fit(X, Y, epochs=1, verbose=0)
# make a prediction
Xnew=tf.random.normal((1, 2))
ynew = model.predict(Xnew)
# show the inputs and predicted outputs
print("X=%s, Predicted=%s" % (Xnew, ynew))
# X=tf.Tensor([[-0.8087067  0.5405918]], shape=(1, 2), dtype=float32), Predicted=[[-0.02120915 -0.0466493 ]]

我认为问题出在你的输入格式上。为什么不使用4作为输入尺寸？
我尝试了不同的格式（numpy）。输出相当不错。

import numpy as np
raw = np.array([[41.392953, 41.392889, 41.392825,41.392761, 41.392697],
        [2.163917,2.163995,2.164072,2.164150,2.164229 ]])
# example of making predictions for a regression problem
from keras.models import Sequential
from keras.layers import Dense
X = raw[:,:-1]
y = raw[:,-1]
# define and fit the final model
model = Sequential()
model.add(Dense(4, input_dim=4, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='adam')
model.fit(X, y, epochs=1000, verbose=0)
# make a prediction
Xnew=X[len(X)-1:len(X)]
ynew = model.predict(Xnew)
# show the inputs and predicted outputs
print("X=%s, Predicted=%s" % (Xnew, ynew))

输出：

X=[[2.163917 2.163995 2.164072 2.16415 ]], Predicted=[[2.3935468]]