Python LSTM回归问题与掩蔽和直觉(keras)

我正在使用这种架构(一个用于不同轨迹长度的掩蔽层，填充0到最大长度轨迹，然后是一个LSTM，之后是一个密集层，输出2个值)来构建一个回归器，根据轨迹预测2个值.

samples, timesteps, features = x_train.shape[0], x_train.shape[1], x_train.shape[2]

model = Sequential()
model.add(tf.keras.layers.Masking(mask_value=0., input_shape=(timesteps, features), name="mask"))
model.add(LSTM(30, return_sequences=True, name="lstm1"))
model.add(LSTM(30, return_sequences=False, name="lstm2"))
model.add(Dense(20, activation='relu', name="dense1"))
model.add(Dense(20, activation='relu', name="dense2"))
model.add(Dense(2, activation='linear', name="output"))
model.compile(optimizer="adam", loss="mse")

培训内容包括:

model.fit(x_train, y_train, epochs = 10, batch_size = 32)

我的输入数据形状如下:

x_train (269, 527, 11) (269 trajectories of 527 timesteps of 11 features)
y_train (269, 2) (these 269 trajectories have 2 target values)
x_test (30, 527, 11) (--- same ---)
y_test (30, 2) (--- same ---)

我已经对我的数据进行了预处理，所以我的所有序列都有固定的长度，较小的序列在缺失的时间步中填充0.

正如预期的那样，输出的形状如下:

(30, 2)

但研究它似乎是在回归同样的价值观.

[[37.48257     0.7025466 ]
 [37.48258     0.70254654]
 [37.48257     0.70254654]
 [37.48257     0.7025466 ]
 [37.48258     0.70254654]
 [37.48258     0.70254654]
 [37.48258     0.70254654]
 [37.48258     0.7025465 ]
 [42.243515    0.6581909 ]
 [37.48258     0.70254654]
 [37.48257     0.70254654]
 [37.48258     0.70254654]
 [37.48261     0.7025462 ]
 [37.48257     0.7025466 ]
 [37.482582    0.70254654]
 [37.482567    0.70254654]
 [37.48257     0.7025466 ]
 [37.48258     0.70254654]
 [37.48258     0.70254654]
 [37.48257     0.7025466 ]
 [37.48258     0.70254654]
 [37.48258     0.70254654]
 [37.48258     0.70254654]
 [37.482567    0.7025465 ]
 [37.48261     0.7025462 ]
 [37.482574    0.7025466 ]
 [37.48261     0.7025462 ]
 [37.48261     0.70254624]
 [37.48258     0.70254654]
 [37.48261     0.7025462 ]]

而我的目标值(y_测试)是:

[[70.   0.6]
 [40.   0.6]
 [ 6.   0.6]
 [94.   0.7]
 [50.   0.6]
 [60.   0.6]
 [16.   0.6]
 [76.   0.9]
 [92.   0.6]
 [32.   0.8]
 [22.   0.7]
 [70.   0.7]
 [36.   1. ]
 [64.   0.7]
 [ 0.   0.9]
 [82.   0.9]
 [38.   0.6]
 [54.   0.8]
 [28.   0.8]
 [62.   0.7]
 [12.   0.6]
 [72.   0.8]
 [66.   0.8]
 [ 2.   1. ]
 [98.   1. ]
 [20.   0.8]
 [82.   1. ]
 [38.   1. ]
 [68.   0.6]
 [62.   1. ]]

这就像将整个数据集作为一个数据点.

感谢任何帮助！